{"id":"W4411509208","doi":"10.1038/s41597-025-05332-x","title":"A minimum data standard for wildlife disease research and surveillance","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Metadata; Wildlife; Data sharing; Wildlife disease; Computer science; Transparency (behavior); Best practice; Data science; Table (database); Data element; Data mining; Information retrieval; World Wide Web; Ecology; Biology; Medicine","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":[0.006929251,0.00009362099,0.0002166797,0.0002239863,0.0004600164,0.0004385639,0.001280412,0.00003399365,0.0001093825],"category_scores_gemma":[0.00506663,0.00007774946,0.00001699018,0.0007257211,0.00057191,0.000321566,0.002030568,0.0001316762,0.00001592068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003858765,"about_ca_system_score_gemma":0.002927645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004807762,"about_ca_topic_score_gemma":0.00009606041,"domain_scores_codex":[0.9973575,0.00008456725,0.0002513782,0.001216193,0.0006032732,0.0004870321],"domain_scores_gemma":[0.9930891,0.0004173036,0.00003439965,0.005471641,0.0003726823,0.0006148499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004928952,0.0001317298,0.005577037,0.0005878308,0.00002463648,0.000008389075,0.00001902538,7.695692e-8,0.000008131886,0.002468896,0.9611055,0.02957588],"study_design_scores_gemma":[0.001080518,0.00006427489,0.0104761,0.0001467176,0.00002698495,0.000001071971,0.0001997291,0.006505108,0.000001142789,0.002386,0.97904,0.00007236642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1368937,0.0135176,0.01233393,0.2550463,0.008485946,0.008596753,0.5448068,0.0003438063,0.01997514],"genre_scores_gemma":[0.7441849,0.0004084953,0.007410771,0.004529777,0.0008652409,0.000106971,0.1634622,0.00005944354,0.07897215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6072913,"threshold_uncertainty_score":0.6065595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2307392565385012,"score_gpt":0.4785848791230941,"score_spread":0.2478456225845929,"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."}}