{"id":"W4385569148","doi":"10.1016/j.anscip.2023.04.064","title":"O63 Drowning in data, thirsting for knowledge","year":2023,"lang":"en","type":"article","venue":"Animal - science proceedings","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Data science; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.003353681,0.000420924,0.0005402216,0.002186883,0.002379922,0.006799454,0.001068886,0.00175268,0.03167422],"category_scores_gemma":[0.02048649,0.0003296039,0.0007479834,0.003486433,0.003102979,0.00529733,0.003966463,0.002125195,0.005857206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002162094,"about_ca_system_score_gemma":0.002164509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01570106,"about_ca_topic_score_gemma":0.01926664,"domain_scores_codex":[0.9965025,0.0008628435,0.000291894,0.0007450067,0.001300919,0.0002968717],"domain_scores_gemma":[0.9893036,0.004369776,0.001063028,0.003108293,0.001492682,0.0006625152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004088116,0.0001528465,0.05504722,0.0003733933,0.00009673506,0.001887674,0.004485551,0.003408362,0.005257932,0.1030191,0.1090954,0.716767],"study_design_scores_gemma":[0.00003218553,0.0001700881,0.03423508,0.0006367878,0.00007684032,0.003391879,0.008733929,0.02445475,0.008975079,0.1736641,0.7455146,0.0001146052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3226053,0.008364376,0.2122812,0.08984053,0.005240154,0.0003768291,0.009411066,0.002973215,0.3489073],"genre_scores_gemma":[0.7530578,0.003620503,0.06577747,0.006277533,0.001223136,0.0001208658,0.006933603,0.0008234316,0.1621657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03167422,"threshold_uncertainty_score":0.1059608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051264354813173,"score_gpt":0.367997448282477,"score_spread":0.2628710128011597,"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."}}