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Taxon groups are the verbatim names found in the abstracts (case-insensitive), not normalised: maize, corn and Zea mays are separate groups here. The Organism view merges them through NCBI Taxonomy.

not stated 98

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Model parameters fell from 9,413,574 to 2,297,334, a 75.6% decrease.

Outcome
number of model parameterse0
Direction
reducede0
Value
2,297,334e0
Qualifier
a decrease of 75.6%e0
Comparator
YOLOv11se0

Not reportedTaxon

Verbatim

e0

“number of model parameters has been substantially reduced from 9,413,574 in YOLOv11s to 2,297,334 (a decrease of 75.6%)”

✓ located verbatim in the source · abstract, characters 1931–2050

Title

YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11

Yue Liao; Lerong Li; Huiqiang Xiao; et al. Agronomy 2025. doi:10.3390/agronomy15030687

Other claims from this paper (15)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

In the chamber test, leaf temperature slowed down after a rapid decrease, similar to natural conditions.

Outcome
leaf temperature slowed down after a rapid decreasee0
Comparator
the natural conditione0

Verbatim

e0

“leaf temperature slowed down after a rapid decrease which is similar to the natural condition”

✓ located verbatim in the source · abstract, characters 1434–1527

Title

Design and experiment on artificial radiation-frost chamber based on temperature difference between leaves and air dew point

Yongzong Lu; Yongguang Hu; Jintao Tian; et al. 2018 Detroit, Michigan July 29 - August 1, 2018 2018. doi:10.13031/aim.201800331

Other claims from this paper (4)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Degree of pectin methyl esterification can be determined by measuring a decrease in galacturonic acid with conventional colorimetric methods.

Outcome
decrease in galacturonic acide4
Direction
decreasee4
Qualifier
conventional colorimetric methodse4
Comparator
gas chromatographye4

Not reportedTaxon

Verbatim

e4

“The degree of pectin methyl esterification can then be determined by measuring an increase of galactose using gas chromatography (borohydride reduced samples) or gas chromatography/mass spectroscopy (borodeuteride reduced samples), or by measuring the decrease in galacturonic acid using conventional colorimetric methods”

✓ located verbatim in the source · abstract, characters 414–735

Title

A new method for measuring degree of methyl esterification in pectin

Niels O. Maness; James D. Ryan; Andrew J. Mort Plant Physiology, Supplement; (USA) 1989. doi:10.1016/j.leukres.2008.03.037

Other claims from this paper (4)
Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

A curve decrease at 420 nm preceded pollen abortion, suggesting a marker.

Outcome
marker associated with pollen abortione0
Direction
decreasee0
Value
420 nme0

Verbatim

e0

“a steep decrease of multispectral curves at 420 nm appeared before the occurrence of abortion, implying a marker associated with pollen abortion in rice”

✓ located verbatim in the source · abstract, characters 976–1128

Title

Study of the Changes in Insoluble Polysaccharides during Pollen Development in Rice (Oryza sativa L.) by Microscopic Multispectral Imaging

Shunan Liu; Lei Pan; Qiongshui Wu; et al. Microscopy and Microanalysis 2010. doi:10.1017/s1431927610000413

Other claims from this paper (4)
Method / resourceobservedoriginal resultsimulated

Claim · model-written summary

The rice silicon model reproduced a gradual decrease in transporter gene expression observed previously.

Outcome
expression level of the transporter genese0
Direction
gradual decreasee0
Comparator
previous experimental studiese0

Verbatim

e0

“The model reproduced the gradual decrease and diurnal variation of the expression level of the transporter genes observed by previous experimental studies”

✓ located verbatim in the source · abstract, characters 809–963
t0

“Rice”

✓ located verbatim in the source · title, characters 31–35

Title

A Model of Silicon Dynamics in Rice: An Analysis of the Investment Efficiency of Si Transporters

Gen Sakurai; Naoki Yamaji; Namiki Mitani-Ueno; et al. Frontiers in Plant Science 2017. doi:10.3389/fpls.2017.01187

Other claims from this paper (3)
Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

ePE6d precise small edits did not decrease significantly relative to ePE2.

Outcome
precise edits by ePE6de0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

t0

“rice”

✓ located verbatim in the source · title, characters 60–64
e0

“a significant decrease in precise edits by ePE6d was not observed throughout the targets”

✓ located verbatim in the source · abstract, characters 2001–2089

Title

Engineering PE6 prime editors to efficiently insert tags in rice

Rongfang Xu; Chong Ma; Jiaqi Sheng; et al. Plant Biotechnology Journal 2024. doi:10.1111/pbi.14456

Other claims from this paper (19)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Missing data caused only a minor decrease in selection coincidence.

Taxon
maizeZea mayst0
Outcome
coincidencee0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“missing data only led to a minor decrease in coincidence.”

✓ located verbatim in the source · abstract, characters 1314–1371
t0

“Maize Hybrids”

✓ located verbatim in the source · title, characters 92–105

Title

Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids

Giovanni Galli; Danilo Hottis Lyra; Filipe Couto Alves; et al. Crop Science 2018. doi:10.2135/cropsci2017.07.0459

Other claims from this paper (3)
Method / resourceobservedoriginal resultnegativeprojected

Claim · model-written summary

The 4M model predicted a considerable future decrease in maize yields.

Taxon
maizeZea mayse0
Outcome
maize yieldse0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“4M predicted a considerable decrease of maize yields as well as yield safety for the future.”

✓ located verbatim in the source · abstract, characters 933–1025

Title

USING LONG-TERM FIELD EXPERIMENT DATA TO PREPARE A CROP SIMULATION MODEL FOR CLIMATE IMPACT STUDIES

GY. MICSKEI Applied Ecology and Environmental Research 2016. doi:10.15666/aeer/1403_263280

Other claims from this paper (6)
Method / resourceobservedoriginal resultnegativeprojected

Claim · model-written summary

The 4M model predicted a considerable future decrease in maize yield safety.

Taxon
maizeZea mayse0
Outcome
yield safetye0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“4M predicted a considerable decrease of maize yields as well as yield safety for the future.”

✓ located verbatim in the source · abstract, characters 933–1025

Title

USING LONG-TERM FIELD EXPERIMENT DATA TO PREPARE A CROP SIMULATION MODEL FOR CLIMATE IMPACT STUDIES

GY. MICSKEI Applied Ecology and Environmental Research 2016. doi:10.15666/aeer/1403_263280

Other claims from this paper (6)
Method / resourceobservedoriginal resultno effectmeasured

Claim · model-written summary

The viscosity maximum and its decrease are not a reliable indicator of amylolytic activity in wheat flour.

Outcome
reliable indicator of amylolytic activitye2
Qualifier
Neither the viscosity maximum nor the decrease in viscosity maximume2
Comparator
amylolytic activitye2

Verbatim

e2

“Neither the viscosity maximum nor the decrease in viscosity maximum was a reliable indicator of amylolytic activity”

✓ located verbatim in the source · abstract, characters 796–911
t0

“Wheat Flour”

✓ located verbatim in the source · title, characters 18–29

Title

Factors Affecting Wheat Flour Amylographic Maximum Viscosity

BLAŽENKA ŠEBEČIČ Journal of Food Science 1989. doi:10.1111/j.1365-2621.1989.tb04664.x

Other claims from this paper (3)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Estimates from images before harvest showed only a slight decrease in performance compared with the statistic obtained just before harvest.

Outcome
performancee1
Direction
slight decreasee1
Comparator
the statistic obtained just before the harveste1

Verbatim

t0

“Wheat Yield Estimates”

✓ located verbatim in the source · title, characters 67–88
e1

“only a slight decrease in performance is observed compared to the statistic obtained just before the harvest”

✓ located verbatim in the source · abstract, characters 1342–1450

Title

Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Remy Fieuzal; Vincent Bustillo; David Collado; et al. Agronomy 2020. doi:10.3390/agronomy10030327

Other claims from this paper (7)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Sources of Rht-B1b, Rht-D1b and Rht8c alleles that decrease wheat height were identified for selection.

Gene
Rht-B1b, Rht-D1b and Rht8ce0
Outcome
sourcese0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“The sources of the Rht-B1b, Rht-D1b and Rht8c alleles, which lead to a decrease in the plant height and can be used in the selection process of wheat, have been identified”

✓ located verbatim in the source · abstract, characters 483–654

Title

A STUDY OF THE ALLELIC COMPOSITION OF THE RHT1, RHT2, RHT8 DWARFING GENES IN THE COLLECTION OF WINTER WHEAT (TRITICUM AESTIVUM L.) VARIETIES AND LINES AND THEIR INFLUENCE ON AGRONOMIC TRAITS

E. A. Fomina; S. V. Malyshev; S. N. Kulinkovich; et al. Proceedings of the National Academy of Sciences of Belarus Biological Series 2018. doi:10.29235/1029-8940-2018-63-1-46-52

Other claims from this paper (1)

soybean 18

Show all 18 →
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The hyperactive mmPing20 version negated the decrease in transposition rate caused by promoter embedding.

Taxon
soybeanGlycine maxe4
Treatment
a hyperactive version of mPinge1
Outcome
transposition ratee1
Direction
negatede1
Comparator
Embedding promoter sequences in mPinge1

Verbatim

e1

“Embedding promoter sequences in mPing resulted in a decrease in overall transposition rate; however, this effect was negated by using a hyperactive version of mPing called mmPing20”

✓ located verbatim in the source · abstract, characters 375–555
e4

“Expression analysis of a line that contained a heritable insertion of the mmPing20F activation tag indicated that the activation tag induced overexpression of the nearby soybean genes”

✓ located verbatim in the source · abstract, characters 709–892

Title

Development of mPing ‐based activation tags for crop insertional mutagenesis

Alexander Johnson; Edward Mcassey; Stephanie Diaz; et al. Plant Direct 2021. doi:10.1002/pld3.300

Other claims from this paper (4)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

SLW-YOLO detection speed decreased by 42.1 FPS relative to YOLOv5s.

Taxon
SoybeanGlycine maxt0
Outcome
detection speede0
Direction
decreasee0
Value
42.1 FPSe0

Verbatim

t0

“Soybean”

✓ located verbatim in the source · title, characters 19–26
e0

“There was a decrease in detection speed by 42.1 FPS”

✓ located verbatim in the source · abstract, characters 980–1031

Title

SLW-YOLO: A Hybrid Soybean Parent Phenotypic Consistency Detection Model Based on Deep Learning

Chuntao Yu; Jinyang Li; Wenqiang Shi; et al. Agriculture 2025. doi:10.3390/agriculture15192001

Other claims from this paper (8)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The model reproduced the 2007 decrease in leaf area index and biomass.

Taxon
soybeanGlycine maxt0
Outcome
reduction in the leaf area index (LAI) and in biomass production during this year, which was reproduced by the modele0

Not reportedValue

Verbatim

e0

“There was a reduction in the leaf area index (LAI) and in biomass production during this year, which was reproduced by the model.”

✓ located verbatim in the source · abstract, characters 526–655
t0

“soybean growth and yield under northeastern Amazon climatic conditions”

✓ located verbatim in the source · title, characters 14–84

Title

Simulation of soybean growth and yield under northeastern Amazon climatic conditions

Paulo Jorge de Oliveira Ponte de Souza; José Renato Bouças Farias; José Paulo Mourão de Melo e Abreu; et al. Pesquisa Agropecuária Brasileira 2011. doi:10.1590/s0100-204x2011000600001

Other claims from this paper (5)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

All four models had a slight accuracy decrease on workstations and mobile devices.

Taxon
Plantt0
Outcome
accuracye1
Direction
decreasee0
Comparator
workstation and mobile devicese0

Verbatim

e0

“the four models have a slight accuracy decrease on both workstation and mobile devices”

✓ located verbatim in the source · abstract, characters 765–851
e1

“Mason model has a highest accuracy of 90.54%”

✓ located verbatim in the source · abstract, characters 567–611
t0

“Plant Diseases”

✓ located verbatim in the source · title, characters 40–54

Title

Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device

Teddy Aristan; Gede Putra Kusuma Revue d intelligence artificielle 2023. doi:10.18280/ria.370221

Other claims from this paper (7)
Method / resourceobservedoriginal resultsimulated

Claim · model-written summary

A mechanics equation describes plant deflection and a dimensionless parameter represents its reduction by turgor.

Taxon
plante0
Outcome
decrease in deflectione0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“We derived an equation describing the plant's consequent deflection, introducing a dimensionless parameter to express the decrease in deflection associated with the action of turgor pressure”

✓ located verbatim in the source · abstract, characters 660–850

Title

Rigidity control mechanism by turgor pressure in plants

Tohya Kanahama; Satoru Tsugawa; Motohiro Sato Scientific Reports 2023. doi:10.1038/s41598-023-29294-5

Other claims from this paper (1)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Processing time decreased significantly.

Taxon
plantt0
Outcome
processing timee0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“significant decrease in processing time”

✓ located verbatim in the source · abstract, characters 1110–1149
t0

“plant leaf diseases”

✓ located verbatim in the source · title, characters 84–103

Title

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

S.M. Nuruzzaman Nobel; Maharin Afroj; Md Mohsin Kabir; et al. Artificial Intelligence in Agriculture 2024. doi:10.1016/j.aiia.2024.10.005

Other claims from this paper (5)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Precision decreased slightly in later correction iterations because of false positives.

Outcome
Precisione1
Direction
decreasee1
Qualifier
false positivese1

Not reportedValue

Verbatim

e1

“a slight decrease in Precision was observed in later iterations due to false positives”

✓ located verbatim in the source · abstract, characters 1350–1436
t0

“Tomato Detection”

✓ located verbatim in the source · title, characters 51–67

Title

Automatic Correction of Labeling Errors Applied to Tomato Detection

Ángel Eduardo Zamora Suárez; Gerardo Antonio Alvarez Hernandez; Juan Irving Vasquez; et al. Agriculture 2025. doi:10.3390/agriculture15121291

Other claims from this paper (3)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Decreased fruit growth and sap-flow readings mark the need for the next watering.

Outcome
need for the next wateringe0
Direction
decreasee0

Not reportedValue

Verbatim

e0

“A decrease in their indications marks the need for the next watering”

✓ located verbatim in the source · abstract, characters 1079–1147
t0

“tomato”

✓ located verbatim in the source · title, characters 67–73

Title

The use of the phytomonitoring method to control the irrigation of tomato plantations: A case study

Andrii Shatkovskyi; Olexandr Zhuravlov; Volodymyr Vasiuta Journal of Water and Land Development 2022. doi:10.24425/jwld.2022.141551

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed model reduced the number of FLOPs by 7.94% compared with the original Yolov11n.

Outcome
number of FLOPse2
Direction
decreasee2
Value
7.94%e2
Comparator
original version of Yolov11ne2

Verbatim

e1

“improve the detection of tomato leaves in images of complex outdoor environments”

✓ located verbatim in the source · abstract, characters 700–780
e2

“led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n”

✓ located verbatim in the source · abstract, characters 911–1054

Title

Deep Learning Architecture for Tomato Plant Leaf Detection in Images Captured in Complex Outdoor Environments

Andros Meraz-Hernández; Jorge Fuentes-Pacheco; Andrea Magadán-Salazar; et al. Mathematics 2025. doi:10.3390/math13152338

Other claims from this paper (2)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Combined-view images decreased classification accuracy by 3%.

Treatment
images with combined viewse0
Outcome
accuracye0
Direction
decreasee0
Value
3%e0

Verbatim

e0

“using images with combined views shows a decrease in accuracy by 3% while requiring only 60% of the memory for weights.”

✓ located verbatim in the source · abstract, characters 1682–1801
t0

“Apple Cultivar Classification”

✓ located verbatim in the source · title, characters 10–39

Title

Enhancing Apple Cultivar Classification Using Multiview Images

Silvia Krug; Tino Hutschenreuther Journal of Imaging 2024. doi:10.3390/jimaging10040094

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Decreased growth rate and growth break were relatively reliable early indicators of natural fruit drop.

Outcome
early evaluation of natural fruit drope0
Direction
decreasee0
Qualifier
relatively reliablee0

Not reportedComparatorValue

Verbatim

t0

“apple”

✓ located verbatim in the source · title, characters 15–20
e0

“a decrease in growth rate and fruit growth break can be used as a relatively reliable method for the early evaluation of natural fruit drop”

✓ located verbatim in the source · abstract, characters 150–289

Title

Growth rate of apple fruits as a method of predicting their abscission.

D. Janković; Jovana Janković; R. Blagojević Acta agriculturae Serbica 2009

Other claims from this paper (7)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Its model size was 11.5% below YOLOv8n.

Outcome
model sizee0
Direction
decreasee0
Value
11.5%e0
Comparator
YOLOv8n benchmark modele0

Verbatim

e0

“Relative to the YOLOv8n benchmark model, this represents a 15.6% reduction in parameter volume and an 11.5% decrease in model size”

✓ located verbatim in the source · abstract, characters 1799–1929
t0

“Apple”

✓ located verbatim in the source · title, characters 0–5

Title

Apple Leaf Disease Detection Based on Lightweight YOLOv8-GSSW

Wei Zeng; Jicheng Pang; Kaicheng Ni; et al. Applied Engineering in Agriculture 2024. doi:10.13031/aea.16022

Other claims from this paper (5)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Water and fertilizer usage decreased by 25%.

Taxon
cropt0
Outcome
water and fertilizer usagee0
Direction
decreasee0
Value
25%e0

Not reportedComparator

Verbatim

e0

“25% decrease in water and fertilizer usage”

✓ located verbatim in the source · abstract, characters 749–791
t0

“Crop”

✓ located verbatim in the source · title, characters 90–94

Title

Transforming Precision Agriculture with Quantum Computing: A Novel Algorithm for Boosting Crop Yields and Optimizing Resources

Anshit Mukherjee; Biswadip Basu Mallik EPJ Web of Conferences 2025. doi:10.1051/epjconf/202532501004

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Time-Series Driven RL slightly decreased RMSE relative to SUBPLEX.

Taxon
cropt0
Outcome
RMSEe0
Direction
decreasee0
Comparator
SUBPLEXe1

Not reportedValue

Verbatim

e0

“a slight decrease in RMSE, while significantly reducing the computational burden by 365 times”

✓ located verbatim in the source · abstract, characters 1156–1249
e1

“compared to 0.76 for SUBPLEX”

✓ located verbatim in the source · abstract, characters 1122–1150
t0

“crop”

✓ located verbatim in the source · title, characters 56–60

Title

Reinforcement learning-based assimilation of the WOFOST crop model

Haochong Chen; Xiangning Yuan; Jian Kang; et al. Smart Agricultural Technology 2024. doi:10.1016/j.atech.2024.100604

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AI methods decreased fertilizer application by 20% without sacrificing output.

Taxon
Cropt0
Outcome
fertilizer applicatione0
Direction
decreasee0
Value
20%e0
Qualifier
without sacrificing outpute0

Verbatim

e0

“a 20% decrease in fertilizer application, all without sacrificing output”

✓ located verbatim in the source · abstract, characters 633–705
t0

“Crop Recommendation and Yield Maximization”

✓ located verbatim in the source · title, characters 36–78

Title

Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization

Jathar Raj Girish INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2025. doi:10.55041/ijsrem50964

Other claims from this paper (2)
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Feature importance suggests one camera may suffice without a significant accuracy decrease.

Outcome
accuracye0
Direction
without significant decreasee0
Comparator
one camerae0

Verbatim

e0

“Feature weight importance suggests that one camera may be sufficient for prediction without significant decrease in accuracy”

✓ located verbatim in the source · abstract, characters 991–1115
t0

“Corn Ear Height Prediction”

✓ located verbatim in the source · title, characters 10–36

Title

Automated Corn Ear Height Prediction Using Video-Based Deep Learning

Johnson Wong; Hao Sha; Mohammad Al Hasan; et al. 2020 IEEE International Conference on Big Data (Big Data) 2020. doi:10.1109/bigdata50022.2020.9378115

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Increasing replications from five to six gave only a small decrease in L.S.D.

Outcome
L.S.D.e0
Direction
decreasee0
Value
5 to 6e0

Verbatim

e0

“There was only a small decrease in L.S.D. by increasing the replications from 5 to 6.”

✓ located verbatim in the source · abstract, characters 391–476
t0

“Hybrid Corn”

✓ located verbatim in the source · title, characters 26–37

Title

Field Plot Technique With Hybrid Corn Under Alabama Conditions1

A. A. Fleming; T. Hayden Rogers; T. A. Bancroft Agronomy Journal 1957. doi:10.2134/agronj1957.00021962004900010001x

Other claims from this paper (4)
Method / resourceobservedoriginal resultnegativesimulated

Claim · model-written summary

The SDI models indicated a linear decline in relative yield with increasing wetness.

Outcome
relative yielde0
Direction
linear decreasee0
Comparator
increasing wetness (SDI values)e0

Verbatim

e0

“The SDI models indicated a linear decrease in the relative yield with increasing wetness (SDI values)”

✓ located verbatim in the source · abstract, characters 823–924
t0

“CORN”

✓ located verbatim in the source · title, characters 0–4

Title

CORN GROWTH AS AFFECTED BY EXCESS SOIL WATER

S. Mukhtar; J. L. Baker; R. S. Kanwar Transactions of the ASAE 1990. doi:10.13031/2013.31348

Other claims from this paper (9)

Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.