{"id":"W4410459179","doi":"10.59297/bz4zhp86","title":"Risk Modelling for Remote Communities: An Inuit-driven Bayesian Network Approach to Enhance Search and Rescue Operations in Arctic Canada","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International ISCRAM Conference","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Environment Research Council; National Research Council Canada","keywords":"Search and rescue; Arctic; Bayesian network; The arctic; Computer science; Bayesian probability; Geography; Environmental resource management; Environmental science; Oceanography; Artificial intelligence; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.002553278,0.0007785236,0.0006362558,0.001237779,0.001113459,0.001977829,0.001870202,0.001438621,0.001879354],"category_scores_gemma":[0.007975369,0.0005230313,0.0007115724,0.0008693496,0.001140255,0.00133936,0.001958283,0.001307719,0.0001128309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007449289,"about_ca_system_score_gemma":0.00476157,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4254277,"about_ca_topic_score_gemma":0.3807916,"domain_scores_codex":[0.9990282,0.0005590462,0.00002238784,0.0001304321,0.000114594,0.0001454219],"domain_scores_gemma":[0.9970543,0.002002894,0.0002495008,0.00006173427,0.0004605647,0.0001709864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002178526,0.00002528894,0.002456985,0.00001263783,0.00002443416,0.0000870202,0.000180666,0.9792553,0.0001095504,0.01442032,0.000309624,0.003096454],"study_design_scores_gemma":[0.000003384137,0.000005367337,0.000280243,0.000005453722,0.000006708295,0.000007634572,0.00008512186,0.9935663,0.00002877485,0.005679079,0.0003260992,0.000005833979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3929293,0.0005892431,0.5834446,0.003550906,0.00005126062,0.0002247268,0.000641531,0.0001681659,0.01840028],"genre_scores_gemma":[0.9559473,0.0002935681,0.03857769,0.0001117841,0.00002644013,0.0001078534,0.0002253593,0.00003526593,0.004674708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5745723,"threshold_uncertainty_score":0.8459029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939091971131457,"score_gpt":0.2590104308173349,"score_spread":0.2396195111060203,"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."}}