{"id":"W2382677389","doi":"","title":"Progress of Crop Model Research","year":2012,"lang":"en","type":"article","venue":"Jilin Nongye Daxue xuebao","topic":"Environmental and Agricultural Sciences","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Crop; Maturity (psychological); Yield (engineering); Agricultural engineering; Computer science; Growth model; Crop yield; Mathematics; Agronomy; Engineering; Biology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009294473,0.0001250817,0.0001388133,0.0000282865,0.0002122045,0.00002398475,0.0004610642,0.0000729924,0.001386848],"category_scores_gemma":[0.00002582665,0.00007885866,0.00006121984,0.0004677214,0.0009494308,0.0006093741,0.000465583,0.0001862565,0.0008172427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096583,"about_ca_system_score_gemma":0.000006211968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002119802,"about_ca_topic_score_gemma":0.00004450825,"domain_scores_codex":[0.997945,0.00007969064,0.0002214642,0.0002768654,0.0008528309,0.0006241948],"domain_scores_gemma":[0.9994112,0.00004743965,0.00006680658,0.0002431679,0.000008797879,0.0002225784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004061083,0.0009943953,0.7423501,0.00003705155,0.0000147011,0.000005267579,0.003032245,0.005558344,0.2013235,0.001052392,0.01964068,0.02595071],"study_design_scores_gemma":[0.0006178956,0.0003362754,0.7662563,0.00006616763,0.00002732177,0.00002502546,0.001194014,0.0140587,0.1564572,0.002263892,0.05780745,0.000889732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709452,0.0001916256,0.0000167079,0.00030924,0.00008814678,0.000213153,0.00001277206,0.00002116886,0.02820198],"genre_scores_gemma":[0.9921278,0.00002981311,0.002833874,0.00004438627,0.00008385938,0.00002986106,0.000007737702,0.000006977659,0.004835648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0448663,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05541418501878408,"score_gpt":0.3238975866342011,"score_spread":0.268483401615417,"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."}}