{"id":"W3108834841","doi":"10.3390/su122310133","title":"Impacts of Climate and Phenology on the Yields of Early Mature Rice in China","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Montréal","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Phenology; Transplanting; Oryza sativa; Sunshine duration; Growing season; Agronomy; Precipitation; Oryza; Maturity (psychological); Biology; Animal science; Environmental science; Sowing; Geography","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":[0.0004882558,0.0003429372,0.0002104887,0.0006258159,0.0002739031,0.0003692808,0.0001508104,0.000117405,0.0002658592],"category_scores_gemma":[0.0005704315,0.0001599835,0.0003764749,0.0008247591,0.0002464783,0.0002317335,0.0003905737,0.0001259823,0.00004800677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007170321,"about_ca_system_score_gemma":0.0006772894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03955759,"about_ca_topic_score_gemma":0.06929076,"domain_scores_codex":[0.9997868,0.00003910866,0.0000221616,0.00006104816,0.00004586926,0.00004502937],"domain_scores_gemma":[0.9994562,0.00008937578,0.0001832676,0.00004835424,0.0001166294,0.0001061331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004770225,0.00001481848,0.9836434,0.00002280917,0.00007485747,0.0001481042,0.0002139468,0.001417566,0.007918208,0.00004656506,0.00008788844,0.006364159],"study_design_scores_gemma":[7.172814e-7,0.000005493168,0.9994747,5.475837e-7,0.000005853021,0.00001057991,0.00003063881,0.0003279251,0.00009302224,0.000005840057,0.00004316075,0.000001589625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999631,0.00005977391,0.00005591945,0.00001303718,0.000001184726,0.000001009258,0.0001146403,0.000003867272,0.0001194919],"genre_scores_gemma":[0.999513,0.00007704333,0.00005954424,0.000007319747,0.000002896315,0.000001882023,0.0002429204,0.000001757456,0.00009366899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03955759,"threshold_uncertainty_score":0.07865465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858836165769308,"score_gpt":0.247221178876887,"score_spread":0.2286328172191939,"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."}}