{"id":"W4405985827","doi":"10.1016/j.onehlt.2024.100964","title":"Discovering topics and trends in biosecurity law research: A machine learning approach","year":2024,"lang":"en","type":"article","venue":"One Health","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biosecurity; Data science; MEDLINE; Computer science; Psychology; Medicine; Political science; Pathology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0105712,0.0004448703,0.0007004327,0.0298763,0.001252671,0.005164792,0.0007401938,0.001032515,0.001592099],"category_scores_gemma":[0.03195828,0.000334089,0.00129047,0.02169121,0.001330693,0.005190678,0.001679109,0.001214108,0.0004014336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002721014,"about_ca_system_score_gemma":0.001956855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007181606,"about_ca_topic_score_gemma":0.009259755,"domain_scores_codex":[0.9938052,0.003364279,0.0007725377,0.0009294678,0.0007897937,0.0003387238],"domain_scores_gemma":[0.9415244,0.04871615,0.004958802,0.001491766,0.002874558,0.0004343263],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002937233,0.0003993623,0.5987594,0.001157353,0.0006432927,0.0006355199,0.01105565,0.01539464,0.002345753,0.02381237,0.004298183,0.3412048],"study_design_scores_gemma":[0.0001063528,0.0003762878,0.4984874,0.0008760354,0.0005384862,0.0007356489,0.03067532,0.3360761,0.002452904,0.100915,0.02858766,0.0001728857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7995296,0.009612762,0.1672773,0.005900032,0.0001452991,0.0006927284,0.005366687,0.0004591776,0.01101648],"genre_scores_gemma":[0.9471838,0.001441203,0.04765641,0.0002246839,0.0001692883,0.0004367322,0.002032159,0.00003277471,0.0008229157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9894288,"threshold_uncertainty_score":0.05590647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2063168542472727,"score_gpt":0.3735197074285053,"score_spread":0.1672028531812326,"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."}}