{"id":"W4406020796","doi":"10.1186/s40066-024-00509-w","title":"The impact of precipitation, temperature, and soil moisture on wheat yield gap quantification: evidence from Morocco","year":2025,"lang":"en","type":"article","venue":"Agriculture & Food Security","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Fondation OCP","keywords":"Yield (engineering); Precipitation; Environmental science; Agronomy; Moisture; Yield gap; Water content; Winter wheat; Soil science; Crop yield; Geography; Geology; Biology; Materials science; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002754238,0.0003653977,0.0003516654,0.00001645852,0.0005689414,0.0002958891,0.0004969329,0.0003611867,0.00006106136],"category_scores_gemma":[0.0005904639,0.00009736794,0.0002294652,0.0008577385,0.0001219108,0.0002783107,0.0001118674,0.0004514,0.000008096417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008468675,"about_ca_system_score_gemma":0.00002280633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002237059,"about_ca_topic_score_gemma":0.01530791,"domain_scores_codex":[0.9981509,0.0001709056,0.0003971399,0.0005535037,0.0003703189,0.0003571968],"domain_scores_gemma":[0.9974183,0.001541842,0.0002475921,0.0002063632,0.0004393441,0.0001465644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002973373,0.0003279773,0.01614053,0.00003780055,0.0002307757,0.000001990008,0.001961491,0.00003566661,0.867206,0.001462627,0.1095067,0.002791113],"study_design_scores_gemma":[0.0001924196,0.001064123,0.9465293,0.0005211012,0.00007327406,0.000006274336,0.002335389,0.00001064863,0.04363668,0.003667568,0.001620724,0.0003425514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837156,0.006934227,3.933184e-7,0.007283516,0.0002105375,0.0006034693,0.0005190449,0.00008135431,0.0006518547],"genre_scores_gemma":[0.9972879,0.001627066,0.000007190323,0.0002177684,0.0003279468,0.00004922616,0.0002593298,0.000001539735,0.0002220594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9303887,"threshold_uncertainty_score":0.8542175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542848732868213,"score_gpt":0.2856345947415389,"score_spread":0.2402061074128568,"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."}}