{"id":"W2746975986","doi":"","title":"糞便に汚染された亜熱帯海水におけるBacteroidalesのDNAマーカーと培養可能Escherichia coli(大腸菌)との間の相対的濃度の追跡 新鮮および年数を経た汚染の識別における利用の可能性","year":2017,"lang":"ja","type":"article","venue":"Canadian Journal of Microbiology","topic":"Mycobacterium research and diagnosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Escherichia coli; DNA; Microbiology; Biology; Escherichia coli Proteins; Genetics; Gene","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":[0.0009225133,0.0002275203,0.0002303053,0.0005886607,0.0009066971,0.001234331,0.0002429105,0.0007265717,0.003278681],"category_scores_gemma":[0.00130574,0.0002422268,0.0002752694,0.0004861604,0.0013263,0.0005643734,0.0004262438,0.000707,0.001663996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226099,"about_ca_system_score_gemma":0.001224369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00813877,"about_ca_topic_score_gemma":0.01029723,"domain_scores_codex":[0.9990815,0.0001455075,0.0000661444,0.0001420246,0.0004042955,0.0001604682],"domain_scores_gemma":[0.9992082,0.0001885961,0.0001497082,0.00006146192,0.0002917067,0.0001003294],"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":[0.000806749,0.0004727397,0.07968146,0.0009658709,0.0001251179,0.002106895,0.00349492,0.0007178215,0.6225908,0.04099943,0.01041278,0.2376254],"study_design_scores_gemma":[0.00008534441,0.001353833,0.1028287,0.0003247324,0.0001917821,0.004353748,0.004304057,0.001026721,0.6730753,0.01226992,0.2000777,0.0001081435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.798251,0.01713871,0.02529396,0.008437781,0.001514625,0.0003829543,0.0009833716,0.0003009432,0.1476968],"genre_scores_gemma":[0.931582,0.005434599,0.02172045,0.00167146,0.0003162689,0.00007114268,0.0004554843,0.00002638261,0.03872221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00813877,"threshold_uncertainty_score":0.01618278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02541960329645248,"score_gpt":0.2780251720528439,"score_spread":0.2526055687563914,"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."}}