{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007660063,0.0001595581,0.0001129885,0.0001387471,0.0004346373,0.0002440782,0.0004393891,0.00003001266,0.005029679],"category_scores_gemma":[0.000004966234,0.0001518273,0.00001851914,0.0005706943,0.002579093,0.0008908671,0.001574508,0.0001461735,0.0002377727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004217656,"about_ca_system_score_gemma":0.000005710376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007261107,"about_ca_topic_score_gemma":0.0009536155,"domain_scores_codex":[0.9980005,0.00004842629,0.0002008843,0.0007341729,0.0005021653,0.0005138763],"domain_scores_gemma":[0.9993601,0.00001551797,0.00002989145,0.0004095742,0.000001287486,0.0001836684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002125102,0.0003007791,0.4552304,0.000009998284,0.000003357212,0.00005593478,0.001116959,0.00004696304,0.001750835,0.0005422418,0.0002816767,0.5406396],"study_design_scores_gemma":[0.000587878,0.0001966338,0.6054116,0.0000108329,0.000006690276,0.00001711251,0.008071895,0.01055966,0.0003107765,0.0007826247,0.3736432,0.0004010535],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7404813,0.00002375233,0.0006831866,0.00006072364,0.0000753958,0.0002747047,9.417591e-7,0.00002255573,0.2583775],"genre_scores_gemma":[0.9884888,0.00008007907,0.001710299,0.00007007468,0.00001202616,0.00004737425,0.000009977733,0.00001366469,0.009567688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5402386,"threshold_uncertainty_score":0.9958798,"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."}}