{"id":"W2944713968","doi":"10.1017/s1049023x19001031","title":"Learning Lessons during Recovery from Disasters","year":2019,"lang":"en","type":"article","venue":"Prehospital and Disaster Medicine","topic":"Disaster Response and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; École Nationale d'Administration Publique; Public Health Ontario","funders":"","keywords":"Preparedness; Process (computing); Natural disaster; Emergency management; Best practice; Tacit knowledge; Public relations; Environmental resource management; Environmental planning; Political science; Knowledge management; Geography; Computer science; Environmental science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005468033,0.001007116,0.0004633106,0.0008357104,0.003816479,0.003505522,0.003268423,0.002300127,0.006627914],"category_scores_gemma":[0.0133153,0.0002475284,0.0005814925,0.00061776,0.004501571,0.003274324,0.006296271,0.003071261,0.001676048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003821618,"about_ca_system_score_gemma":0.009560695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004558035,"about_ca_topic_score_gemma":0.01580854,"domain_scores_codex":[0.9958795,0.002180664,0.0001267729,0.0002806759,0.0007145723,0.0008178153],"domain_scores_gemma":[0.994552,0.002424467,0.0005438809,0.0003536297,0.0007125129,0.001413544],"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":[0.0002296708,0.002737857,0.007525865,0.003341861,0.00005266678,0.004907806,0.2804735,0.003830962,0.003620179,0.01267518,0.1044648,0.5761396],"study_design_scores_gemma":[0.0001354228,0.002364159,0.01420401,0.003690966,0.00006788621,0.003855099,0.5124028,0.002393542,0.006164339,0.03508023,0.4194476,0.0001938733],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6526107,0.004756993,0.06450116,0.06239055,0.002438743,0.004411558,0.0006933292,0.00220497,0.2059921],"genre_scores_gemma":[0.9086073,0.003996753,0.05744472,0.004545417,0.0002544776,0.0009056014,0.0005094054,0.0001128931,0.02362342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006627914,"threshold_uncertainty_score":0.02891803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02217803246886881,"score_gpt":0.3388793455320159,"score_spread":0.3167013130631471,"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."}}