{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003720626,0.0001081433,0.0001147344,0.000472035,0.0004842604,0.0006219507,0.003042548,0.00003701639,0.000001682219],"category_scores_gemma":[0.002117605,0.0001019129,0.00001806003,0.003790317,0.0001422256,0.003306699,0.001302544,0.0001651432,0.0001192389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005166394,"about_ca_system_score_gemma":0.0001640095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002579243,"about_ca_topic_score_gemma":0.000007829634,"domain_scores_codex":[0.9980108,0.000005802081,0.000227059,0.0008962724,0.0003244297,0.000535697],"domain_scores_gemma":[0.9991274,0.0001511557,0.0001053582,0.0003419315,0.0001786793,0.00009540686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002068069,0.0000851103,0.03388169,0.0001687579,0.000003462452,0.000003342942,0.010061,0.00001596549,0.1782124,0.6503074,0.008186193,0.119054],"study_design_scores_gemma":[0.000181466,0.00008002276,0.05977501,0.00005205685,0.000001863107,0.000006204272,0.0005143099,0.9251783,0.001271244,0.002086512,0.01067202,0.0001809277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8940125,0.0001297822,0.05735311,0.009285605,0.0006987394,0.0008319059,0.00001749782,0.002004978,0.03566595],"genre_scores_gemma":[0.9713513,0.000006423041,0.02823104,0.00007360017,0.00009339914,0.00003063441,0.00001246997,0.000007730033,0.0001934446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9251624,"threshold_uncertainty_score":0.5997484,"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."}}