View: List Claim type Research area Organism Taxon as written Decade Paper Method / resource observed original result measured Claim · model-written summary
NDVI and EVI were more effective at detecting spatial and temporal vine vigor variability during mid-to-late seasons than during early seasons.
detecting spatial and temporal vine vigor variability e1 more effective e1 early seasons e1 Verbatim
t0 “grapevine ”
✓ located verbatim in the source · title, characters 43–52 e1 “more effective at detecting spatial and temporal vine vigor variability during mid-to-late seasons than during early seasons ”
✓ located verbatim in the source · abstract, characters 1770–1894 Other claims from this paper (12) Method / resource observed original result measured Claim · model-written summary
Temporal variability in biomass and LAI was mapped for the study area and individual farms.
Temporal variability in biomass and LAI e0 Verbatim
e0 “Temporal variability in biomass and LAI were mapped for the study area and highlighted for individual farms. ”
✓ located verbatim in the source · abstract, characters 1511–1619 e1 “six crops (wheat, barley, chickpea, lentil, vetch and cumin) ”
✓ located verbatim in the source · abstract, characters 290–350 Other claims from this paper (5) Method / resource observed original result positive measured Claim · model-written summary
NDVI yield maps captured temporal and spatial variation in the study area.
temporal and spatial variability e0 Verbatim
e0 “Through yield maps, the NDVI perceived the temporal and spatial variability in the study area. ”
✓ located verbatim in the source · abstract, characters 1515–1609 t0 “Relationship between MODIS-NDVI data and wheat yield : A case study in Northern Buenos Aires province, Argentina ”
✓ located verbatim in the source · title, characters 0–111 Other claims from this paper (5) Method / resource observed original result positive measured Claim · model-written summary
Clustering identified stable spatial and temporal yield clusters.
stable, physically meaningful clusters with recognizable spatial and temporal structure e0 Not reported Comparator
Verbatim
e0 “The clustering procedure successfully identified stable, physically meaningful clusters with recognizable spatial and temporal structure ”
✓ located verbatim in the source · abstract, characters 954–1090 e1 “rice fields ”
✓ located verbatim in the source · abstract, characters 547–558 Other claims from this paper (2) Method / resource observed original result negative measured Claim · model-written summary
The temporal for spatial variability assumption is sensitive to interactions at the soil plant atmosphere interface and fails under some circumstances.
under certain circumstances e0 Not reported Comparator Value
Verbatim
e0 “the assumption is highly sensitive to specific interactions at the soil-plant-atmosphere interface and, consequently, does not hold under certain circumstances. ”
✓ located verbatim in the source · abstract, characters 867–1027 t0 “impact of climate change on crop yield ”
✓ located verbatim in the source · abstract, characters 302–340 Other claims from this paper (1) Method / resource observed original result positive measured Claim · model-written summary
Clustering Sentinel-2 NDVI detected early spatial and temporal crop variation.
crop spatial and temporal variability e0 eight different stages e0 Verbatim
t0 “crop ”
✓ located verbatim in the source · title, characters 46–50 e0 “The cluster analysis of NDVI data from Sentinel-2 collected at eight different stages detected the crop spatial and temporal variability at an early stage ”
✓ located verbatim in the source · abstract, characters 1425–1579 Other claims from this paper (6) Method / resource observed original result positive measured Claim · model-written summary
Machine learning explained 93% of observed yield variability .
observed yield variability e0 Verbatim
e0 “machine learning can explain 93% of observed yield variability ”
✓ located verbatim in the source · abstract, characters 935–997 Other claims from this paper (9) Method / resource observed original result simulated Claim · model-written summary
Simulated yield differed from the long term mean by 21.5%.
percent absolute difference from long-term mean e0 Verbatim
e0 “The percent absolute difference from long-term mean ( temporal variability ) was 21.5% ”
✓ located verbatim in the source · abstract, characters 1730–1814 t0 “crop yield ”
✓ located verbatim in the source · title, characters 79–89 e1 “by corn and soybean phenological phase ”
✓ located verbatim in the source · abstract, characters 1035–1073 Other claims from this paper (10) Method / resource observed original result measured Claim · model-written summary
The method estimated spatial and temporal PPFD variability with 4.4% relative root mean square error.
Not reported Direction
Verbatim
e1 “strawberry fruit yield in a greenhouse ”
✓ located verbatim in the source · abstract, characters 411–449 e0 “This method enabled successful estimation of spatiotemporal variability in PPFD and A with relative root mean square errors of 4.4% and 11.0%, respectively ”
✓ located verbatim in the source · abstract, characters 451–606 Other claims from this paper (9) Method / resource observed original result negative measured Claim · model-written summary
Deep learning architectures encountered more challenges in temporal extrapolation of soybean yield , with RRMSE 51% across years.
temporal extrapolation e2 Verbatim
e0 “predicting within-field soybean yield s ”
✓ located verbatim in the source · abstract, characters 208–246 e2 “The DL architectures encountered more challenges with temporal extrapolation (RRMSE 51 % in across-years) ”
✓ located verbatim in the source · abstract, characters 1000–1105 Other claims from this paper (5) 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.
Vitis
Triticum aestivum
Oryza sativa
Zea mays subsp. mays_single2.jpg?width=60){kind=small}
Fragaria
Glycine max