{"id":"W2025419334","doi":"10.1007/s002670010168","title":"Integrating Local and Scientific Knowledge: An Example in Fisheries Science","year":2001,"lang":"en","type":"article","venue":"Environmental Management","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":170,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sociology of scientific knowledge; Knowledge base; Set (abstract data type); Task (project management); Computer science; Resource (disambiguation); Environmental resource management; Data science; Ecology; Artificial intelligence; Engineering; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03443653,0.0008914877,0.001644909,0.008997202,0.01106694,0.02138247,0.003586023,0.009990901,0.006098319],"category_scores_gemma":[0.03341248,0.0008468847,0.001746611,0.0187582,0.02747138,0.02610759,0.01585461,0.006905813,0.0008263324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007743129,"about_ca_system_score_gemma":0.01085446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02243341,"about_ca_topic_score_gemma":0.03595588,"domain_scores_codex":[0.9831524,0.01171299,0.0006488675,0.000628299,0.003019047,0.0008384662],"domain_scores_gemma":[0.9585223,0.03211205,0.0009740136,0.003119478,0.003800444,0.001471827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001034604,0.0002358727,0.004422837,0.0006376453,0.0001472651,0.002393395,0.04524795,0.002427958,0.000799739,0.8357405,0.007179833,0.1006635],"study_design_scores_gemma":[0.00007125338,0.00007297852,0.002487897,0.0005060303,0.0001144261,0.0006839838,0.0259175,0.004096304,0.000730445,0.8669425,0.09830386,0.00007291278],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08042702,0.02476239,0.3116198,0.1677916,0.0008666539,0.0003236485,0.0001835016,0.0004015305,0.413624],"genre_scores_gemma":[0.7383547,0.01100781,0.2244819,0.00762547,0.0005567886,0.0003687445,0.0001615214,0.0002532564,0.01718973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03443653,"threshold_uncertainty_score":0.18212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455100430519429,"score_gpt":0.2417924120907164,"score_spread":0.2172414077855221,"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."}}