{"id":"W4366492899","doi":"10.1093/database/baad023","title":"CARD*Shark: automated prioritization of literature curation for the Comprehensive Antibiotic Resistance Database","year":2023,"lang":"en","type":"article","venue":"Database","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research; Cisco Systems Canada; Canada Foundation for Innovation; McMaster University; Cisco Systems","keywords":"Computer science; Database; Scope (computer science); Prioritization; Task (project management); Information retrieval; Triage; World Wide Web; Data science; Medicine; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007104265,0.002295293,0.002741294,0.02727049,0.001997215,0.006813718,0.002749753,0.00109429,0.05431666],"category_scores_gemma":[0.03875874,0.001313948,0.001865973,0.01855944,0.0006727092,0.004203411,0.006870868,0.001408431,0.03679406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001837096,"about_ca_system_score_gemma":0.01007538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006098967,"about_ca_topic_score_gemma":0.01398694,"domain_scores_codex":[0.9944627,0.001023578,0.001339496,0.001055854,0.001777737,0.0003406848],"domain_scores_gemma":[0.97183,0.01144516,0.003337115,0.004155307,0.00741807,0.001814356],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007646186,0.0001462249,0.005149499,0.009658682,0.0003736204,0.0004957484,0.0004648202,0.0006895459,0.006178774,0.004305182,0.7893844,0.1823888],"study_design_scores_gemma":[0.000737078,0.0002234227,0.01895056,0.001980309,0.0004314149,0.001075553,0.0005637378,0.01204228,0.01751095,0.007018618,0.9391574,0.000308654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01032158,0.003562423,0.06148912,0.001866834,0.0006503733,0.003664288,0.7747621,0.1204672,0.02321618],"genre_scores_gemma":[0.01894401,0.002391926,0.3217109,0.0008368368,0.0002986874,0.003728411,0.6357222,0.007649935,0.008717063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9928957,"threshold_uncertainty_score":0.1817073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199321420995732,"score_gpt":0.3013782340759962,"score_spread":0.281446091976423,"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."}}