{"id":"W3158267591","doi":"10.1002/vetr.481","title":"DNA profiling for juvenile kidney disease in boxers","year":2021,"lang":"en","type":"article","venue":"Veterinary Record","topic":"Renal and related cancers","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Citation; Profiling (computer programming); Juvenile; Miller; Computer science; Library science; Genetics; Biology; Ecology; Programming language","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":[0.0005257324,0.0001530667,0.0001802972,0.00264627,0.0006162091,0.0006516312,0.0002468167,0.0004612105,0.00310828],"category_scores_gemma":[0.001207775,0.0001525021,0.0001824957,0.00124784,0.0003624814,0.0002717465,0.0003277473,0.0003629457,0.0005640161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003815259,"about_ca_system_score_gemma":0.0004376315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00657918,"about_ca_topic_score_gemma":0.01150858,"domain_scores_codex":[0.999461,0.00008602187,0.00004653668,0.0001287773,0.0001389737,0.0001386899],"domain_scores_gemma":[0.9989884,0.0002381752,0.0004111802,0.00004627235,0.000180404,0.0001354111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004074982,0.00006972397,0.899455,0.0001287185,0.00003548693,0.002437501,0.0009385759,0.00006964727,0.07815936,0.0001808254,0.0005670954,0.01755055],"study_design_scores_gemma":[0.000004021891,0.0002262642,0.9679356,0.00006479944,0.00003788697,0.003301875,0.001761134,0.0001454137,0.02131851,0.00006795218,0.005121534,0.00001496266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952067,0.0008921973,0.0005421079,0.0001207304,0.00002304634,0.00002936227,0.001244676,0.00002128099,0.001919902],"genre_scores_gemma":[0.992682,0.0007915561,0.001390249,0.0001200953,0.00001822539,0.00001956622,0.0008374895,0.00001379493,0.004127035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00657918,"threshold_uncertainty_score":0.01308179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754649956499371,"score_gpt":0.2698248126081474,"score_spread":0.2522783130431537,"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."}}