{"id":"W2354747303","doi":"10.5539/jas.v8n6p33","title":"Mapping Chinese Rice Suitability to Climate Change","year":2016,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Yield gap; Yield (engineering); Agriculture; Sowing; China; Environmental science; Crop; Climate change; Stability (learning theory); Crop yield; Agronomy; Agricultural engineering; Agricultural economics; Geography; Ecology; Economics; Engineering; Computer science; Biology; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009438345,0.0001208684,0.0001655625,0.00003147287,0.0002737505,0.00009890367,0.0005638584,0.00003135537,0.0001453843],"category_scores_gemma":[0.0004118779,0.00002459203,0.000102164,0.001380026,0.00008681649,0.001028905,0.0001452113,0.00007246889,0.00004472022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170233,"about_ca_system_score_gemma":0.00001088112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002915463,"about_ca_topic_score_gemma":0.00003866665,"domain_scores_codex":[0.9984391,0.00003208737,0.0003665129,0.0002110573,0.0005693356,0.0003819572],"domain_scores_gemma":[0.9987473,0.0001309972,0.0002660163,0.00004684966,0.0005149545,0.0002938846],"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":[0.0000077261,0.00004040206,0.02001156,0.000002066747,0.000001806038,9.665764e-7,0.0002286743,2.993571e-7,0.9294653,0.00006138054,0.00018791,0.0499919],"study_design_scores_gemma":[0.0001088346,0.0002450086,0.9894367,0.00005192317,0.000001841909,0.00002485433,0.0005764721,4.857418e-7,0.007595985,0.00005523035,0.001788302,0.00011431],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810915,0.0000271708,0.00001014273,0.01777109,0.0003792243,0.0001764599,0.00000718228,0.00001735362,0.0005199024],"genre_scores_gemma":[0.9984338,0.00004315204,0.000227492,0.0006509339,0.0005483862,0.000005283987,3.859496e-7,3.134134e-7,0.00009026744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9694252,"threshold_uncertainty_score":0.2105495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441748672368886,"score_gpt":0.2589216401366778,"score_spread":0.224504153412989,"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."}}