{"id":"W2155464656","doi":"10.5539/jas.v7n12p59","title":"Development of Damage Assessment Method of Rice Crop for Agricultural Insurance Using Satellite Data","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Indemnity; Estimation; Agriculture; Linear regression; Regression analysis; Yield (engineering); Satellite; Regression; Paddy field; Statistics; Environmental science; Computer science; Agricultural engineering; Mathematics; Engineering; Actuarial science; Agronomy; Geography; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006590723,0.0005458713,0.0004456347,0.001529507,0.0002987031,0.0005228813,0.0005560788,0.0004647627,0.001625632],"category_scores_gemma":[0.001552307,0.0002607151,0.000580574,0.000884681,0.0001757848,0.0008432668,0.0005089525,0.0004453549,0.000553165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003515562,"about_ca_system_score_gemma":0.0008202904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00539124,"about_ca_topic_score_gemma":0.004179229,"domain_scores_codex":[0.9996046,0.00006983343,0.00003217524,0.0001017642,0.000164196,0.00002736827],"domain_scores_gemma":[0.9994918,0.00009190167,0.00005910322,0.00004666447,0.000284127,0.00002635741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001300086,0.0001140068,0.01876621,0.0002450413,0.0001137364,0.0002137774,0.0002375556,0.09679236,0.05636214,0.002791366,0.004070859,0.8201629],"study_design_scores_gemma":[0.00001636538,0.00007230612,0.01033104,0.00001749129,0.00003132302,0.0001331156,0.00009818807,0.9749016,0.01083924,0.001236715,0.002291248,0.00003143012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04126716,0.0001622437,0.955704,0.0001021374,0.00003324379,0.0001171634,0.0002118868,0.0007651605,0.001636985],"genre_scores_gemma":[0.4225814,0.0004334525,0.5712796,0.00005255129,0.00006128081,0.000330019,0.00076284,0.00006423086,0.004434635],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00539124,"threshold_uncertainty_score":0.01071972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1623163882423015,"score_gpt":0.3678008514653963,"score_spread":0.2054844632230948,"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."}}