{"id":"W2752950956","doi":"","title":"Klebsiella(クレブシエラ)種系統S1のキャラクタリゼーション 生体内変化を介するセコイソラリシレシノールの細菌生産者","year":2017,"lang":"ja","type":"article","venue":"Canadian Journal of Microbiology","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Klebsiella; Microbiology; Biology; Escherichia coli; Genetics","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":[0.0003624247,0.0004303058,0.0003362544,0.0006923688,0.0009305571,0.002166506,0.0003546011,0.0007275236,0.003222788],"category_scores_gemma":[0.0008153951,0.0002471313,0.0003986428,0.0008863878,0.0008810792,0.000761127,0.000726508,0.0004914086,0.002696335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506623,"about_ca_system_score_gemma":0.002199134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0203992,"about_ca_topic_score_gemma":0.01662273,"domain_scores_codex":[0.999072,0.00008955911,0.00008892362,0.00017122,0.0003872333,0.0001911378],"domain_scores_gemma":[0.9993134,0.00006107182,0.0001546996,0.00002858927,0.0003691413,0.00007302163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007441127,0.0001602566,0.05777875,0.002577168,0.0001932581,0.002824934,0.003577852,0.002567353,0.6645194,0.02249853,0.009624181,0.2329343],"study_design_scores_gemma":[0.00005575046,0.001064006,0.09127618,0.0005263364,0.0002643302,0.006396622,0.01064544,0.001656541,0.4258969,0.01093177,0.4510078,0.0002784386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7325417,0.04904247,0.02427063,0.006678883,0.001542488,0.0003701898,0.002746301,0.00055477,0.1822525],"genre_scores_gemma":[0.9335372,0.01456078,0.01108322,0.0008223204,0.000129657,0.00004412367,0.001069597,0.00003367204,0.03871943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0203992,"threshold_uncertainty_score":0.04056096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078492036486602,"score_gpt":0.1997865658316578,"score_spread":0.1890016454667918,"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."}}