“Cluster II”

Claim · model-written summary

SAM-S improved segmentation recall by 11.7%.

Taxon

grapet0

Treatment

SAM-Se1

Outcome

Recalle0

Direction

improvese0

Value

+11.7%e0

Comparator

DepthSege1

Verbatim

e0

“improves segmentation accuracy (IoU: +13.9%, Precision: +9.2%, Recall: +11.7%)”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote

Claim · model-written summary

PhoB positively regulated genes involved in cell motility and chemotaxis in X. citri pv. glycines under phosphate limitation in vitro.

Taxon

SoybeanGlycine maxt0

Treatment

phoB and phoU mutantse6

Interactor

X. citri pv. glycinesXanthomonas citri pv. glycinese6pathogen

Gene

PhoBe6

Outcome

cell motility, and chemotaxise6

Direction

positively regulatede6

Not reportedComparator

Verbatim

e6

“Global gene expression profiles of phoB and phoU mutants of X. citri pv. glycines conducted under phosphate-limitation conditions in vitro, using RNA-seq, revealed that PhoB positively regulated genes involved in signal transduction, the xcs cluster type II secretion system, cell motility, and chemotaxis, while negatively regulating cell wall and membrane biogenesis, DNA replication and recombination and repair, and …” Show full quote

t0

“Virulence in Soybean”

Claim · model-written summary

Hybrid S-8001 was superior to other hybrids and checks for total grains per panicle, productive tillers per plant and grain yield per plant.

Taxon

RiceOryza sativat0

Material

S-8001e0

Outcome

number of total grains per panicle, number of productive tillers per plant and grain yield per plante0

Direction

superiore0

Comparator

other hybrids and checkse0

Verbatim

e0

“the hybrid S-8001 which is present in the cluster II is found to be superior over other hybrids and checks in the present study by having high mean values for the economically important characters viz., number of total grains per panicle, number of productive tillers per plant and grain yield per plant”

t0

“Rice (Oryza sativa L.)”

Claim · model-written summary

Instance segmentation techniques required much less training time than manual labeling.

Taxon

grapet0

Outcome

time for traininge0

Direction

much lesse0

Comparator

manual labelinge0

Verbatim

e0

“instance segmentation techniques require much less time for training than manual labeling”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote

Claim · model-written summary

Seed hardening with MnSO4 at 100 ppm gave the highest number of clusters, pods and pod weight per plant in black gram compared with other treatments and control.

Taxon

black gramVigna mungot0

Treatment

seed hardening with MnSO4@100 ppme2

Outcome

no. of cluster per plant, no. of pods per plant, pod weight per plante1

Direction

higheste1

Comparator

other treatments and controle2

Verbatim

t0

“black gram (Vigna mungo L. hepper) cv. VBN 6”

e1

“seeds hardened with MnSO4@100 ppm resulted in the highest values for various parameters viz., plant height, no. of leaves per plant, no. of branches per plant, no. of nodules per plant, days to first flowering, no. of cluster per plant, no. of pods per plant, pod weight per plant, pod length, no. of seeds per pod, no. of seeds per plant, test weight, pod yield per plant, root length, shoot length, seedling length, se…” Show full quote

e2

“black gram seed hardening with MnSO4@100 ppm improved higher seed yield and quality parameters when compared to other treatments and control”

Claim · model-written summary

ASD improved IoU by 7.8%.

Taxon

grapet0

Treatment

ASDe0

Outcome

IoUe0

Direction

enhancemente0

Value

+7.8%e0

Comparator

DepthSege1

Verbatim

e0

“ASD, although with a lower mAP, shows significant accuracy enhancement (IoU: +7.8%, Precision: +4.2%, Recall: +4.9%)”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote

Claim · model-written summary

SAM-S improved segmentation IoU by 13.9%.

Taxon

grapet0

Treatment

SAM-Se0

Outcome

IoUe0

Direction

improvese0

Value

+13.9%e0

Comparator

DepthSege1

Verbatim

e0

“SAM-S, despite a mAP decrease, improves segmentation accuracy (IoU: +13.9%, Precision: +9.2%, Recall: +11.7%)”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote

Claim · model-written summary

The authors expect crosses among AUR 4, Annamalai mutant ponmani, Karnool sona, Jeeraga samba, AUR 7 and PY 5 to yield heterosis and broad segregation.

Taxon

RICEOryza sativat0

Outcome

produce heterotic combinations and wide variability is segregating generationse0

Verbatim

e0

“Hybridization among genotypes AUR 4, Annamalai mutant ponmani, Karnool sona, Jeeraga samba, AUR 7 and PY 5 from clusters III, II, XII and IX which had maximum inter-cluster distances and desirable values for days to first flower, number of grains per panicle, kernel length, kernel breadth, 1000 grain weight and grain yield per plant is likely to produce heterotic combinations and wide variability is segregating gener…” Show full quote

t0

“RICE”

Claim · model-written summary

SAM-R improved object counting mAP by 0.5% over DepthSeg.

Taxon

grapet0

Treatment

SAM-Re0

Outcome

object countinge0

Direction

advancese0

Value

+0.5%e0

Comparator

DepthSege0

Verbatim

e0

“Compared to the original DepthSeg algorithm, SAM-R slightly advances object counting (mAP: +0.5%)”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote

Claim · model-written summary

AutoSAM-Dino eliminated manual labeling and transfer learning.

Taxon

grapet0

Treatment

AutoSAM-Dinoe0

Outcome

eliminates the need for manual labeling and transfer learninge0

Comparator

manual labeling and transfer learninge0

Verbatim

e0

“AutoSAM-Dino (ASD), which eliminates the need for manual labeling and transfer learning”

t0

“grape bunch”

e1

“Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual lab…” Show full quote