{"id":"W2985762854","doi":"10.1186/s13750-019-0181-3","title":"Bridging Indigenous and science-based knowledge in coastal and marine research, monitoring, and management in Canada","year":2019,"lang":"en","type":"article","venue":"Environmental Evidence","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada; University of Waterloo; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Grey literature; Traditional knowledge; Indigenous; Knowledge base; Environmental resource management; Geography; Bridge (graph theory); Knowledge management; Library science; Ecology; MEDLINE; Computer science; Political science; Biology; Environmental science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412806,0.00009783998,0.0001608312,0.0001740355,0.001091188,0.000009652177,0.0001078896,0.00003679941,0.00006579882],"category_scores_gemma":[0.00002686569,0.0000967268,0.00000434299,0.0001607684,0.0002705528,0.0001293899,0.001118918,0.0003645432,0.0000212405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001743318,"about_ca_system_score_gemma":0.0004929041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7644139,"about_ca_topic_score_gemma":0.9437681,"domain_scores_codex":[0.9981915,0.0001491235,0.0002124462,0.000361267,0.0002331976,0.000852456],"domain_scores_gemma":[0.9994413,0.0002752123,0.00004182789,0.0001349962,0.000006981065,0.00009969106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002614797,0.00002444457,0.983575,0.0001714147,0.000002802169,0.00003903024,0.009734917,0.00001159922,0.0003467861,0.000009479983,0.00001387496,0.006044482],"study_design_scores_gemma":[0.000495072,0.00008525406,0.9729641,0.0002417135,0.000002059314,0.000002091298,0.02547324,0.0002365086,0.00004456813,0.0000115824,0.0003523973,0.00009142097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963956,0.001288247,6.529055e-7,0.0001468416,0.0001763329,0.0009888595,0.000004796815,0.000003529594,0.000995087],"genre_scores_gemma":[0.9962079,0.003193451,0.00007253385,0.00006296324,0.00002515901,0.00008069522,0.000001097902,0.000008414764,0.0003478018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1793543,"threshold_uncertainty_score":0.8392649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05338058786111846,"score_gpt":0.3737978781628583,"score_spread":0.3204172903017398,"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."}}