{"id":"W2131844896","doi":"10.1111/j.1865-1682.2011.01264.x","title":"A Minimum Data Set of Animal Health Laboratory Data to allow for Collation and Analysis across Jurisdictions for the Purpose of Surveillance","year":2011,"lang":"en","type":"article","venue":"Transboundary and Emerging Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Yukon Department of Environment; Ministry of Agriculture; Ministry of Agriculture, Food and Rural Affairs; Government of British Columbia; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Agriculture Food and Rural Development; Canadian Food Inspection Agency","funders":"","keywords":"Data set; Set (abstract data type); Computer science; Data mining; Identification (biology); Disease surveillance; Population; Data sharing; Identifier; Test (biology); Disease; Data science; Environmental health; Medicine; Pathology; Biology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006400914,0.0001388847,0.0004732478,0.00008758204,0.0002814844,0.00002109383,0.0003071387,0.00002837675,0.00001805227],"category_scores_gemma":[0.0001871819,0.0001136472,0.00007097032,0.0004920545,0.0002595747,0.0002504349,0.0001139364,0.00003923617,1.771353e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000979032,"about_ca_system_score_gemma":0.0003011358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002217049,"about_ca_topic_score_gemma":0.0008542663,"domain_scores_codex":[0.9986712,0.00006625259,0.0003972786,0.0004652642,0.0001695182,0.0002305286],"domain_scores_gemma":[0.9980301,0.0003017087,0.0001475801,0.001136295,0.0001689873,0.0002153168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01605978,0.000807634,0.9293041,0.004386868,0.004302075,0.000004889949,0.008316547,0.00005623807,0.001416283,0.0002102871,0.01350707,0.0216283],"study_design_scores_gemma":[0.001583265,0.0005249093,0.9551119,0.00007197635,0.001343989,0.000001140702,0.0006411928,0.008254586,0.00003201463,0.0000214855,0.03226729,0.0001462258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7215491,0.007549873,0.01005786,0.001415061,0.00007368629,0.001366288,0.2579377,0.00004383647,0.000006602894],"genre_scores_gemma":[0.9884618,0.0006347378,0.001860786,0.0001675275,0.00006052148,0.00005083159,0.00873623,0.00001768901,0.000009856548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2669128,"threshold_uncertainty_score":0.4634398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113318864376775,"score_gpt":0.386710351067492,"score_spread":0.2733914866907169,"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."}}