{"id":"W22481207","doi":"10.1039/c2em30068k","title":"地図データの更新とその効率化 : 日本デジタル道路地図協会のデータベースを例として ( 空間データ : 最近の整備動向と新たな活用)","year":2001,"lang":"en","type":"article","venue":"オペレーションズ・リサーチ : 経営の科学","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002847527,0.0003836226,0.0002829414,0.0001436523,0.000432009,0.0004163615,0.0003434715,0.0003722136,0.0008940162],"category_scores_gemma":[0.0001543429,0.0001187059,0.000156772,0.0001826776,0.0004288341,0.0002076927,0.0002712234,0.0002090369,0.0003643043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008166182,"about_ca_system_score_gemma":0.0005779551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02433658,"about_ca_topic_score_gemma":0.04021246,"domain_scores_codex":[0.9997745,0.0000280668,0.000006966612,0.00009260052,0.00006280627,0.00003516091],"domain_scores_gemma":[0.9998747,0.00001863527,0.00002287864,0.000009296167,0.00004639665,0.00002810663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001109001,0.0004936007,0.1020773,0.0003926706,0.00005904476,0.0003420684,0.0005956501,0.0008748227,0.8270639,0.0001014625,0.0002217345,0.0666687],"study_design_scores_gemma":[0.0001089082,0.01756892,0.4450172,0.00004189196,0.0002569334,0.001028992,0.0009799779,0.004286798,0.5186433,0.0001988739,0.01182114,0.00004715464],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976923,0.0001840087,0.001389369,0.00001428197,0.000004978285,0.0000915148,0.0000824715,0.00002176617,0.0005192744],"genre_scores_gemma":[0.9875574,0.0003792245,0.008239424,0.00006209307,0.000008412868,0.00009495886,0.000289188,0.00000760457,0.003361661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02433658,"threshold_uncertainty_score":0.04838985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009186188632421542,"score_gpt":0.2233031622543742,"score_spread":0.2141169736219527,"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."}}