{"id":"W2952332796","doi":"10.5194/nhess-19-2541-2019","title":"Machine learning analysis of lifeguard flag decisions and recorded rescues","year":2019,"lang":"en","type":"article","venue":"Natural hazards and earth system sciences","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flag (linear algebra); FLAGS register; Rip current; Hazard; Warning signs; Geography; Computer science; Transport engineering; Mathematics; Engineering; Fishery; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007103499,0.0001222783,0.0003531695,0.0003107518,0.0002607323,0.0001086116,0.0001538964,0.00004929557,0.0001595847],"category_scores_gemma":[0.00007342335,0.00007847143,0.00007965044,0.0009681021,0.0001758298,0.0002345161,0.00005472928,0.0001386017,0.00001196472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001511871,"about_ca_system_score_gemma":0.00003865197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005426373,"about_ca_topic_score_gemma":0.009910856,"domain_scores_codex":[0.9987395,0.00008211379,0.0002558936,0.000334279,0.0003634961,0.0002247566],"domain_scores_gemma":[0.9993214,0.0002937801,0.0001170846,0.0001069456,0.00005838652,0.0001023895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003018178,0.000002835188,0.6461052,0.00003462515,0.00006278769,0.000002286929,0.0001223677,0.001882904,0.00004588261,0.0004411024,0.000005379604,0.3512645],"study_design_scores_gemma":[0.0001133777,0.000175622,0.3881345,0.0000447173,0.00006632215,0.00001218931,0.0007176363,0.6098617,0.00000609333,0.00003868865,0.0007242002,0.0001049286],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890946,0.00253456,0.00001372589,0.00007514018,0.0003081617,0.0001095535,0.00005778096,0.00002957651,0.007776869],"genre_scores_gemma":[0.9980224,0.0003423482,0.0005824293,0.00001885483,0.00002246252,2.358427e-7,0.00003587534,0.000001413447,0.0009739735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6079788,"threshold_uncertainty_score":0.8203089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008439730248322556,"score_gpt":0.2155018022755027,"score_spread":0.2070620720271802,"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."}}