{"id":"W4406038443","doi":"10.5376/be.2024.14.0027","title":"Development of Precision Agriculture Techniques for Soybean Yield Improvement","year":2024,"lang":"en","type":"article","venue":"Biological Evidence","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Agriculture; Environmental science; Agronomy; Agricultural engineering; Engineering; Materials science; Biology; Ecology; Metallurgy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001340908,0.0003664159,0.0003051063,0.0009998431,0.0002627832,0.0009707055,0.000624287,0.0005099936,0.001686955],"category_scores_gemma":[0.00199735,0.0001958154,0.0005816577,0.001192224,0.0004873359,0.001089338,0.0007034597,0.0006887537,0.0004465086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008754944,"about_ca_system_score_gemma":0.0008156166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002657626,"about_ca_topic_score_gemma":0.004415564,"domain_scores_codex":[0.9993861,0.0001237668,0.00003309653,0.0001351965,0.0002894545,0.00003226689],"domain_scores_gemma":[0.9989876,0.0003941043,0.0002207002,0.0001106259,0.0002583919,0.00002849042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001997764,0.0001707268,0.01007663,0.0009434409,0.00009852738,0.0003009925,0.0003330609,0.0116952,0.2173822,0.014372,0.001437232,0.7429901],"study_design_scores_gemma":[0.0002271998,0.005085742,0.08822537,0.001337463,0.0005789686,0.002953209,0.001307943,0.1192726,0.4451794,0.04112805,0.2943914,0.0003126934],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2644525,0.02589484,0.6643144,0.004337245,0.0003531864,0.0003857726,0.0006025916,0.001036005,0.03862357],"genre_scores_gemma":[0.6380528,0.01393425,0.3413566,0.0004670464,0.0001099849,0.0001426185,0.0003237355,0.00005869412,0.005554256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002657626,"threshold_uncertainty_score":0.007091522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07743226006983346,"score_gpt":0.2818720625050277,"score_spread":0.2044398024351942,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}