{"id":"W3048624358","doi":"10.1007/978-3-030-44975-9_3","title":"Historical Maritime Search and Rescue Incident Data Analysis","year":2020,"lang":"en","type":"book-chapter","venue":"Springer polar sciences","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Search and rescue; Computer science; Exploit; Context (archaeology); Incident report; Guard (computer science); Geography; Data mining; Information retrieval; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006587201,0.0002143552,0.000381993,0.0003395607,0.0001626598,0.000153421,0.0007943392,0.0001652567,0.0004995354],"category_scores_gemma":[0.00004007388,0.0002120589,0.00008950182,0.0002568736,0.0001761694,0.0002088703,0.0005755469,0.0004503979,0.00008057044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781442,"about_ca_system_score_gemma":0.00006567401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005942458,"about_ca_topic_score_gemma":0.000412239,"domain_scores_codex":[0.9982414,0.00001804654,0.0003074625,0.0005947472,0.0006125649,0.0002257905],"domain_scores_gemma":[0.9991585,0.00004694745,0.00002359026,0.0005290193,0.00003309401,0.000208831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009083987,0.0001317786,0.07146184,0.002766282,0.008037381,0.001619204,0.003106335,0.02863662,0.001596935,0.5829194,0.06226275,0.2373707],"study_design_scores_gemma":[0.0001805165,0.00006305861,0.007997224,0.00009229968,0.0006011295,0.00001902196,0.0000194601,0.1815217,0.00003496173,0.001330115,0.8071681,0.0009724197],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00180088,0.02400411,0.006918656,0.003829293,0.001237691,0.0005773524,0.0005557413,0.0009013543,0.9601749],"genre_scores_gemma":[0.5547993,0.007925675,0.02901075,0.0008961835,0.002206564,0.00001492137,0.0007266834,0.0002910987,0.4041288],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7449054,"threshold_uncertainty_score":0.864751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04920393799037787,"score_gpt":0.261560529352218,"score_spread":0.2123565913618402,"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."}}