{"id":"W4224091933","doi":"10.21203/rs.3.rs-1544941/v1","title":"Upcycling yield trial data using a weather-driven crop growth model","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; National Agriculture and Food Research Organization","keywords":"Yield (engineering); Growth model; Crop; Data science; Computer science; Agronomy; Biology; Mathematics; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007785948,0.0004455345,0.0004945188,0.0002868918,0.0002841726,0.0004844278,0.000817918,0.0006944759,0.002917028],"category_scores_gemma":[0.002464516,0.0002508053,0.0005695234,0.0006725999,0.0002298586,0.0005252646,0.0002465441,0.0007495029,0.0005586598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005980483,"about_ca_system_score_gemma":0.0007303815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06237959,"about_ca_topic_score_gemma":0.06198933,"domain_scores_codex":[0.9997979,0.00004556076,0.00001245823,0.00007668398,0.00003525689,0.00003211462],"domain_scores_gemma":[0.9986499,0.0005504766,0.00007760636,0.0002483645,0.0003999398,0.00007381341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006202913,0.0003053479,0.02073363,0.00009877778,0.00009184546,0.00010443,0.00004478218,0.9576038,0.003328012,0.0005725083,0.004715619,0.01178098],"study_design_scores_gemma":[0.00008773786,0.0001055126,0.01938432,0.000007315823,0.00003481859,0.00001292515,0.00002892992,0.9749708,0.003726047,0.0003684882,0.001242819,0.00003033863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982823,0.0000681494,0.004827974,0.0001425121,0.00006061108,0.00004667626,0.009175991,0.0006504849,0.002204779],"genre_scores_gemma":[0.9864419,0.0000350182,0.002829106,0.00002231595,0.00001100742,0.00003630183,0.009328306,0.0001019227,0.001194107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06237959,"threshold_uncertainty_score":0.124033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5525228286252666,"score_gpt":0.4597343597177801,"score_spread":0.09278846890748643,"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."}}