{"id":"W4399563591","doi":"10.1109/cogsima61085.2024.10554045","title":"Cognitive and Computational Aspects of Marine Incident Situation Management System - the Canadian Coast Guard Use Case -","year":2024,"lang":"en","type":"article","venue":"","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coast guard; Guard (computer science); Computer science; Cognition; Engineering; Marine engineering; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001687424,0.0006446153,0.0003055434,0.001642021,0.002019094,0.003907853,0.001559904,0.0006670798,0.003100692],"category_scores_gemma":[0.005616232,0.0003123152,0.0005493365,0.001235247,0.001475853,0.001143769,0.001340597,0.0005662912,0.0004199839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007029833,"about_ca_system_score_gemma":0.01087446,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4713197,"about_ca_topic_score_gemma":0.5227083,"domain_scores_codex":[0.997786,0.0005468811,0.0001484359,0.0002635792,0.0009694067,0.0002856599],"domain_scores_gemma":[0.9983442,0.0006327727,0.0001378318,0.0002304221,0.0005232809,0.0001314833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0008351526,0.0007605348,0.04343772,0.000902732,0.0002225954,0.004643396,0.0141006,0.2369787,0.04022028,0.1626704,0.02571037,0.4695175],"study_design_scores_gemma":[0.00009038025,0.0001579436,0.02101851,0.0002182009,0.0001914021,0.00109325,0.004323299,0.8125924,0.02279809,0.0252103,0.1121071,0.0001990651],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3166696,0.0005754901,0.5335778,0.004405075,0.0001253254,0.002410488,0.002494436,0.003337697,0.1364041],"genre_scores_gemma":[0.7070907,0.0003206409,0.2806931,0.0001449254,0.00002200263,0.000428506,0.001170416,0.00007621768,0.0100535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5286803,"threshold_uncertainty_score":0.9371527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216465101065903,"score_gpt":0.223569614849074,"score_spread":0.211404963838415,"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."}}