{"id":"W2752974703","doi":"10.29244/jitl.19.1.6-12","title":"Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi)","year":2019,"lang":"id","type":"article","venue":"Jurnal Ilmu Tanah dan Lingkungan","topic":"Agricultural Development and Management","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Physics; Horticulture; Forestry; Geography; 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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0009352826,0.00133343,0.001313721,0.0001453748,0.001311223,0.001001654,0.001782862,0.0005409609,0.0008659497],"category_scores_gemma":[0.0001765344,0.0006381114,0.0006860425,0.001522447,0.0002352884,0.0008624007,0.0008899092,0.001369337,0.001889878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004283694,"about_ca_system_score_gemma":0.0001343592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004663322,"about_ca_topic_score_gemma":0.0009293193,"domain_scores_codex":[0.9927339,0.0003653668,0.001395995,0.001954822,0.001523573,0.002026375],"domain_scores_gemma":[0.9970034,0.0002561063,0.0008630761,0.0004851135,0.0005082119,0.0008840926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001047648,0.003778324,0.3390983,0.001420913,0.003680145,0.001284215,0.007260603,0.0002935714,0.3159782,0.01347569,0.07844185,0.2342405],"study_design_scores_gemma":[0.001219087,0.001755846,0.7442113,0.0005237875,0.0004274301,0.00007274345,0.004473337,0.0002282833,0.004394162,0.000201136,0.2403331,0.002159851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694372,0.001960075,0.000004001341,0.0076142,0.003305401,0.002353993,0.00004649023,0.0003562145,0.01492243],"genre_scores_gemma":[0.9439312,0.0008435288,0.0003098408,0.0005994248,0.002050453,0.00006462258,0.0004444433,0.00002871625,0.05172773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.405113,"threshold_uncertainty_score":0.9999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525917962548682,"score_gpt":0.2118638020688605,"score_spread":0.1966046224433737,"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."}}