{"id":"W4388740413","doi":"10.5751/es-14443-280417","title":"Transitioning toward “deep” knowledge co-production in coastal and marine systems: examining the interplay among governance, power, and knowledge","year":2023,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Corporate governance; Panacea (medicine); Production (economics); Environmental resource management; Sustainability; Environmental planning; Political science; Business; Geography; Ecology; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000685501,0.00009673581,0.0001306898,0.00001291197,0.0002409314,0.00002897549,0.00005281576,0.00005935567,0.0000808836],"category_scores_gemma":[0.00001895119,0.00007794631,0.00002055633,0.0001571591,0.0003938731,0.0001249996,0.0008986016,0.000138851,0.00001750105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004374606,"about_ca_system_score_gemma":0.000004752941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003129872,"about_ca_topic_score_gemma":0.004260633,"domain_scores_codex":[0.9992648,0.00008164209,0.000141261,0.0002746189,0.00005033487,0.0001873528],"domain_scores_gemma":[0.99977,0.00008313519,0.00003989385,0.00007130068,0.000003953029,0.00003169221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001927511,0.00008771447,0.8738927,0.0001292852,0.00005113664,0.00001036903,0.03544196,0.0002138487,0.0002250813,0.0006159212,0.01059463,0.07871808],"study_design_scores_gemma":[0.0003104316,0.00006683204,0.9838599,0.00001589573,0.00001081213,0.00001023035,0.007720887,0.003631226,0.000007903308,0.0001902563,0.004081221,0.00009439415],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739785,0.0001281125,0.00004558482,0.0003162194,0.0002690897,0.000204785,0.000003572915,0.00003247541,0.02502169],"genre_scores_gemma":[0.9960767,0.0006183432,0.00001840526,0.00003742368,0.0000278501,0.00003424421,0.000007987352,0.000006010829,0.003173056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1099672,"threshold_uncertainty_score":0.3178558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009667327434243663,"score_gpt":0.2345893243503224,"score_spread":0.2249219969160788,"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."}}