View: List Claim type Research area Organism Taxon as written Decade Paper Organism names are matched through NCBI Taxonomy: “wheat” → Triticum aestivum . Claims whose organism was placed there count as matches whatever the abstract calls it (maize, corn, Zea mays).
Method / resource observed original result positive measured Claim · model-written summary
The proposed method achieved better wheat seeding performance .
wheat seeding performance e0 Not reported Comparator Value
Verbatim
e0 “achieves better wheat seeding performance ”
✓ located verbatim in the source · abstract, characters 1180–1221 Other claims from this paper (3) Method / resource observed original result measured Claim · model-written summary
Adding grayscale images improved wheat seedling detection performance .
seedling detection performance e0 Not reported Comparator Value
Verbatim
e0 “The combination of grayscale images improves wheat seedling detection performance ”
✓ located verbatim in the source · abstract, characters 1132–1213 Other claims from this paper (6) Method / resource observed original result measured Claim · model-written summary
The nursery provided wheat performance data from 40 sites.
Verbatim
e0 “Data were reported from 40 sites. ”
✓ located verbatim in the source · abstract, characters 105–138 t0 “Winter Wheat ”
✓ located verbatim in the source · title, characters 36–48 Other claims from this paper (4) Method / resource observed original result positive measured Claim · model-written summary
YOLOv5 and YOLOv8 models achieved good performance in detecting wheat disease in images.
Verbatim
t0 “Wheat disease monitoring using deep learning ”
✓ located verbatim in the source · title, characters 0–44 e1 “The results show that the YOLOv5 and YOLOV8 models achieved good performance ”
✓ located verbatim in the source · abstract, characters 1179–1255 Other claims from this paper (1) Method / resource observed original result positive measured Claim · model-written summary
Including higher resolution data improved wheat yield forecast performance .
higher resolution data e0 Verbatim
t0 “Wheat yield forecast ”
✓ located verbatim in the source · title, characters 0–20 e0 “Results showed better performance of CNN over BRT, and when higher resolution data was included. ”
✓ located verbatim in the source · abstract, characters 560–656 Other claims from this paper (1) Method / resource observed original result positive measured Claim · model-written summary
The wheat growth stage detection system maintains real-time performance above 12 FPS.
real-time performance e1 Verbatim
t0 “wheat growth stage detection ”
✓ located verbatim in the source · title, characters 10–38 e1 “real-time performance (above 12 FPS) ”
✓ located verbatim in the source · abstract, characters 923–959 Other claims from this paper (3) Method / resource observed original result positive measured Claim · model-written summary
Soil moisture analysis gave acceptable model performance in winter wheat .
soil moisture analysis e3 Acceptable performance e3 Not reported Comparator
Verbatim
t0 “crop specific drought index for winter wheat ”
✓ located verbatim in the source · title, characters 21–65 e3 “Acceptable performance was obtained from the soil moisture analysis ”
✓ located verbatim in the source · abstract, characters 1344–1411 Other claims from this paper (7) Method / resource observed original result positive measured Claim · model-written summary
Genomic selection outperformed marker-assisted selection for predicting wheat hybrid performance .
predicting hybrid performance e0 marker-assisted selection e0 Verbatim
e0 “Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance . ”
✓ located verbatim in the source · abstract, characters 971–1075 t0 “Wheat ”
✓ located verbatim in the source · title, characters 106–111 Other claims from this paper (2) Method / resource author interpretation original result positive measured Claim · model-written summary
MobileNetV2 had superior overall wheat leaf rust detection performance .
overall performance e0 Verbatim
e0 “MobileNetV2 demonstrating superior overall performance ”
✓ located verbatim in the source · abstract, characters 1175–1229 e1 “both MobileNetV2 and ShuffleNet are effective in detecting wheat leaf rust ”
✓ located verbatim in the source · abstract, characters 1094–1168 t0 “Wheat Leaf Rust Detection ”
✓ located verbatim in the source · title, characters 28–53 Other claims from this paper (16) Method / resource observed original result positive measured Claim · model-written summary
The model achieved high performance on real unannotated wheat field datasets.
high performance e0 Not reported Comparator
Verbatim
t0 “Wheat Head Detection ”
✓ located verbatim in the source · title, characters 45–65 e0 “The resulting model achieved high performance when applied to real unannotated datasets. ”
✓ located verbatim in the source · abstract, characters 635–723 Other claims from this paper (2) 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.
Triticum aestivume0