{"id":"W6960420166","doi":"10.1371/journal.pone.0218257.t001","title":"Bacterial growth in 10 μl of milk per cow, quarter and sampling time.","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Sampling (signal processing); Bacterial growth; Milk products","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":[0.001709216,0.002487005,0.002065984,0.003235463,0.0005765537,0.001887322,0.002438371,0.002203421,0.04019439],"category_scores_gemma":[0.007912941,0.0007212418,0.001503582,0.005523078,0.0002927913,0.0007744244,0.00152085,0.001394566,0.04040905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116186,"about_ca_system_score_gemma":0.002037273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0227351,"about_ca_topic_score_gemma":0.03623997,"domain_scores_codex":[0.9986756,0.0002253494,0.0002094565,0.0004841746,0.0002352309,0.0001701755],"domain_scores_gemma":[0.9967924,0.001128364,0.0005856761,0.000687737,0.0005569648,0.0002488222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006257598,0.00007287571,0.00811411,0.005075797,0.0003848876,0.00005655493,0.0000421463,0.0009880309,0.0007817993,0.0005440435,0.976403,0.006910984],"study_design_scores_gemma":[0.001180256,0.0001219333,0.03569142,0.001610323,0.0004045141,0.0001419405,0.0001117799,0.001073301,0.001227209,0.00159957,0.9567607,0.00007709137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001866277,0.00008478916,0.00004230058,0.00001791644,0.000008326745,0.000005134746,0.9993674,0.000103777,0.0001837501],"genre_scores_gemma":[0.0006721226,0.0000704913,0.0002268095,0.00002674137,0.000003406889,0.00005284576,0.9986193,0.00003493155,0.000293342],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04019439,"threshold_uncertainty_score":0.1344636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04102955844360099,"score_gpt":0.2397836464021255,"score_spread":0.1987540879585246,"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."}}