{"id":"W6977125677","doi":"10.6084/m9.figshare.12462824.v1","title":"Additional file 15 of Topography of the respiratory tract bacterial microbiota in cattle","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Academic Research in Diverse Fields","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Calgary","funders":"","keywords":"Table (database); Sampling (signal processing); Respiratory tract; Bacteria; Statistical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001625734,0.001123153,0.001348496,0.002546236,0.00117519,0.001810279,0.001934147,0.001195716,0.8762341],"category_scores_gemma":[0.02049701,0.0006104975,0.0008675395,0.004154345,0.0003204861,0.001599864,0.001062036,0.0007882738,0.1720176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679045,"about_ca_system_score_gemma":0.001469092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385622,"about_ca_topic_score_gemma":0.02275316,"domain_scores_codex":[0.9992361,0.0001313007,0.0001185005,0.0002159972,0.0001664079,0.0001317083],"domain_scores_gemma":[0.984529,0.01131895,0.0009013421,0.0009741283,0.001909264,0.0003673724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003916981,0.0001168341,0.004423954,0.002746851,0.00007920869,0.00008497091,0.0001238003,0.0006152285,0.0003044194,0.0006285546,0.9801869,0.01029756],"study_design_scores_gemma":[0.004414382,0.0004001318,0.07849236,0.004227248,0.0003553352,0.000604794,0.001156355,0.00263212,0.001428518,0.01169054,0.8943633,0.0002348819],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002177761,0.00001244318,0.0001966689,0.00004490791,0.00001835829,0.00003507339,0.9987251,0.0002290962,0.0005204288],"genre_scores_gemma":[0.01084171,0.0001095294,0.003626422,0.0003339556,0.0000774554,0.001147963,0.9740803,0.0009029144,0.008879785],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8762341,"threshold_uncertainty_score":0.176537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08850213909784703,"score_gpt":0.3146798413761389,"score_spread":0.2261777022782918,"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."}}