{"id":"W4406705204","doi":"10.1093/fshmag/vuae026","title":"Advancing black bass management and conservation to benefit fish populations, fisheries, and people","year":2025,"lang":"en","type":"article","venue":"Fisheries","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Ministry of Natural Resources and Forestry","funders":"","keywords":"Fishery; Bass (fish); Fisheries management; Fish <Actinopterygii>; Catch and release; Geography; Recreational fishing; Biology; Fishing","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":[0.0001120078,0.0001171501,0.0001360634,0.0000432577,0.000341333,0.00005673979,0.00007600117,0.00003861567,0.0005789545],"category_scores_gemma":[0.00004557115,0.0001238039,0.00001199879,0.0002269517,0.0001426672,0.0003006453,0.0005193192,0.00004567169,0.00002107853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005411478,"about_ca_system_score_gemma":0.000001844622,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002994134,"about_ca_topic_score_gemma":0.03687283,"domain_scores_codex":[0.9992715,0.00001615684,0.0001498941,0.0002820432,0.00008426748,0.000196148],"domain_scores_gemma":[0.9997417,0.00003763853,0.00003329139,0.0001402535,0.000006988396,0.00004015923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001179166,0.00001050877,0.6694906,0.00003870778,0.00001586251,0.00000127357,0.0003293889,0.00002748041,0.000002725644,0.00161515,0.3270463,0.00141021],"study_design_scores_gemma":[0.0001454672,0.0000230244,0.7835996,0.00001439085,0.00002436529,3.518302e-7,0.0006836205,0.0001198098,0.000007499131,0.003516656,0.2117679,0.00009739579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8882588,0.00002386295,0.0005323003,0.04920993,0.0001889295,0.0004611632,0.000009203511,0.00007131643,0.06124448],"genre_scores_gemma":[0.9259475,0.0003775854,0.008256948,0.02147065,0.00002073696,0.0001924114,0.00003713839,0.00001570582,0.04368126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1152784,"threshold_uncertainty_score":0.9807017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008397259897605588,"score_gpt":0.2122383145239155,"score_spread":0.2038410546263099,"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."}}