{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003564637,0.001107613,0.001535143,0.001822038,0.0007146025,0.003166333,0.002953475,0.001354197,0.005897216],"category_scores_gemma":[0.00664628,0.0006636094,0.002314639,0.003270897,0.0009921168,0.005504427,0.001675386,0.002680969,0.002885557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002666909,"about_ca_system_score_gemma":0.003463076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167565,"about_ca_topic_score_gemma":0.003879476,"domain_scores_codex":[0.9985245,0.0004150698,0.00008717477,0.0003495018,0.0005355632,0.00008825149],"domain_scores_gemma":[0.9970046,0.001064969,0.0001263581,0.0006486243,0.001037006,0.0001185411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001740052,0.0002023601,0.004512247,0.002508579,0.0002598962,0.0001736615,0.0002924188,0.253558,0.005533278,0.2241899,0.04539952,0.4631961],"study_design_scores_gemma":[0.00004376889,0.0001308412,0.002200576,0.0006488173,0.0001433582,0.0002207856,0.0001950828,0.349016,0.004635138,0.0833012,0.5593468,0.0001177515],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04408722,0.1402272,0.6715887,0.01426718,0.003774272,0.0002717699,0.005367516,0.00454432,0.1158718],"genre_scores_gemma":[0.3358549,0.2546238,0.3519756,0.002483436,0.002485595,0.0005364728,0.01422992,0.003344667,0.03446572],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01167565,"threshold_uncertainty_score":0.02321541,"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."}}