{"id":"W4410014692","doi":"10.1016/j.aiia.2025.04.006","title":"Decoding canola and oat crop health and productivity under drought and heat stress using bioelectrical signals and machine learning","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence in Agriculture","topic":"Magnetic and Electromagnetic Effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Field Crop Research Alliance","keywords":"Canola; Productivity; Crop productivity; Decoding methods; Drought stress; Crop; Agronomy; Stress (linguistics); Heat stress; Agricultural engineering; Environmental science; Biology; Mathematics; Engineering; Economics; Statistics; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002198007,0.0001743087,0.00020381,0.00005930174,0.0002051527,0.00008215874,0.00004764704,0.0001371989,0.000002964274],"category_scores_gemma":[0.0001292991,0.0001362642,0.00001540929,0.0002299395,0.0001420136,0.000009107551,0.0001026758,0.0002207787,1.515086e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001872873,"about_ca_system_score_gemma":0.00005601177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00108389,"about_ca_topic_score_gemma":0.002016555,"domain_scores_codex":[0.99882,0.0001450564,0.0002051387,0.0004705118,0.00007031773,0.000288953],"domain_scores_gemma":[0.9996848,0.00005251854,0.0000411785,0.00008031029,0.00003644247,0.0001047347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008366018,0.00007546758,0.02068789,0.0001396198,0.00002308409,0.000004256581,0.000343199,0.0003660823,0.9053094,0.001255714,0.00005470029,0.07165693],"study_design_scores_gemma":[0.0002974864,0.002381478,0.01372602,0.0003593344,0.00006612148,0.0001998598,0.001591497,0.02567797,0.9490446,0.00492001,0.0009369954,0.0007985996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766484,0.01861721,0.003140735,0.001216493,0.00003820203,0.0002678181,0.000003815821,0.000009135869,0.00005820436],"genre_scores_gemma":[0.9969588,0.002131156,0.0004927462,0.0001938221,0.00006659947,0.000007468014,0.00001589135,0.000006533816,0.0001269938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07085834,"threshold_uncertainty_score":0.5556693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875664211780132,"score_gpt":0.2873420323842223,"score_spread":0.268585390266421,"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."}}