{"id":"W2051446596","doi":"10.1016/j.marpol.2010.10.019","title":"Marine mammal co-management in Canada’s Arctic: Knowledge co-production for learning and adaptive capacity","year":2010,"lang":"en","type":"article","venue":"Marine Policy","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"ArcticNet; Marine Mammal Commission","keywords":"Knowledge management; Context (archaeology); Production (economics); Knowledge production; Adaptive management; Knowledge creation; Business; Process (computing); Knowledge sharing; Environmental resource management; Computer science; Geography; Environmental science; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001797929,0.0001740462,0.0002097409,0.001220547,0.00939074,0.005589999,0.001153211,0.001389998,0.00688678],"category_scores_gemma":[0.005623027,0.0001455129,0.0001898182,0.001542294,0.005249174,0.002194502,0.004035223,0.001216467,0.0002679752],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03837015,"about_ca_system_score_gemma":0.1038498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9457908,"about_ca_topic_score_gemma":0.9792435,"domain_scores_codex":[0.9982594,0.0004285435,0.00002573665,0.0001225131,0.0002577983,0.0009060775],"domain_scores_gemma":[0.9932046,0.001257143,0.0005353086,0.000182522,0.001627733,0.003192747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004324383,0.0006010417,0.4979032,0.0004729919,0.000158878,0.001596885,0.08075638,0.003459046,0.001983071,0.1125825,0.03370693,0.2663465],"study_design_scores_gemma":[0.000035782,0.0001300996,0.560109,0.0007194708,0.0001188267,0.0003139448,0.2981949,0.004798365,0.001115152,0.03522918,0.09914877,0.00008649964],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8111866,0.002387102,0.0007310585,0.06116696,0.0001122275,0.00004082318,0.0002200099,0.00002078243,0.1241344],"genre_scores_gemma":[0.9955491,0.0004622116,0.0002372826,0.000413945,0.00001760182,0.000006454526,0.00001975558,0.000002403494,0.003291345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9906093,"threshold_uncertainty_score":0.2783962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091075940553984,"score_gpt":0.3734504337134673,"score_spread":0.3325396743079274,"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."}}