{"id":"W2886992206","doi":"10.1177/0300985818785705","title":"Observational Study Design in Veterinary Pathology, Part 1: Study Design","year":2018,"lang":"en","type":"article","venue":"Veterinary Pathology","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observational study; Causality (physics); Selection (genetic algorithm); Medicine; Computer science; Pathology; Management science; Medical physics; Data science; Medical education; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002413061,0.0003971734,0.0005912452,0.0005802663,0.0004878616,0.00003476584,0.0004776951,0.0003670279,0.002107578],"category_scores_gemma":[0.0002373833,0.0003832337,0.0001016316,0.0005813062,0.0006762764,0.0001980668,0.0005434872,0.0005434226,0.001741307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009709268,"about_ca_system_score_gemma":0.0001983076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009735968,"about_ca_topic_score_gemma":0.00002693547,"domain_scores_codex":[0.9914763,0.005884331,0.000702937,0.0009755655,0.00007924876,0.0008816366],"domain_scores_gemma":[0.9982849,0.0005248922,0.0001411303,0.0007852719,0.0001935646,0.00007023203],"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":[0.004130214,0.01057729,0.05275545,0.00002136172,0.0002296364,0.009342837,0.003039961,0.00006734905,0.9115573,0.00007422598,0.004935908,0.0032685],"study_design_scores_gemma":[0.01670223,0.1720386,0.7590415,0.00008217468,0.0002271284,0.01409511,0.006139692,0.0001009613,0.00576889,0.0007116433,0.02330407,0.001788008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992856,0.0004503353,0.002496908,0.00007790785,0.001353827,0.002347092,0.00004482331,0.000149704,0.000223362],"genre_scores_gemma":[0.9973461,0.00003468814,0.0007338284,0.0001927252,0.0001514217,0.0007206218,0.00006508842,0.00004562162,0.0007098783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9057884,"threshold_uncertainty_score":0.999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2746908880698031,"score_gpt":0.3861679257146809,"score_spread":0.1114770376448778,"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."}}