{"id":"W4221135919","doi":"10.1111/faf.12658","title":"Exploring the potential impacts of machine learning on trust in fishery management","year":2022,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transparency (behavior); Fisheries management; Corporate governance; Enforcement; Context (archaeology); Business; Knowledge management; Control (management); Trust management (information system); Fishery; Computer science; Process management; Political science; Fishing; Computer security; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003440225,0.0001007497,0.0001220116,0.00004882692,0.0003054494,0.00004345809,0.0002204972,0.00001371996,0.006828191],"category_scores_gemma":[0.00002643898,0.00007999491,0.00003553831,0.0002841576,0.0001523104,0.0002233538,0.0008162819,0.0003330637,0.000004185437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005556474,"about_ca_system_score_gemma":0.000003674985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001135356,"about_ca_topic_score_gemma":0.0003298796,"domain_scores_codex":[0.9988945,0.000109639,0.0001666867,0.0002048149,0.0003661279,0.000258249],"domain_scores_gemma":[0.999698,0.00003900392,0.00004331074,0.0001692726,0.000002549489,0.00004780601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003081266,0.0001078906,0.876788,0.00004148015,0.0000201551,0.00008357358,0.001558266,0.0006762108,0.00008806618,0.00008998471,0.005270361,0.1149679],"study_design_scores_gemma":[0.0004190789,0.0003503063,0.7005042,0.000004433616,0.000005723922,0.000009571122,0.002404212,0.001062352,0.00005960935,0.0001371778,0.294906,0.0001372732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8600026,0.000006916221,0.000003742102,0.00141526,0.00008248235,0.0001567124,0.00001061152,0.00001454642,0.1383071],"genre_scores_gemma":[0.9956759,0.0002946447,0.00003965107,0.0003818606,0.00001986481,0.0001048878,0.00001474182,0.0000123114,0.003456106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2896357,"threshold_uncertainty_score":0.9940797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698236142599875,"score_gpt":0.2122714502397381,"score_spread":0.1852890888137393,"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."}}