{"id":"W2754622395","doi":"10.5055/jem.2017.0331","title":"Defining a risk-informed framework for whole-of-government lessons learned: A Canadian perspective","year":2017,"lang":"en","type":"article","venue":"Journal of Emergency Management","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Security Establishment; Defence Research and Development Canada","funders":"","keywords":"Best practice; Process management; Preparedness; Process (computing); Emergency management; Knowledge management; Government (linguistics); Computer science; Business; Political 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":[0.05830484,0.003968702,0.00192635,0.03085188,0.02386246,0.04214115,0.0139467,0.0102231,0.005853891],"category_scores_gemma":[0.06065395,0.00172025,0.001933026,0.01962928,0.07172912,0.02237834,0.01870259,0.01331769,0.0009361824],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.3161207,"about_ca_system_score_gemma":0.4327598,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9234677,"about_ca_topic_score_gemma":0.9028668,"domain_scores_codex":[0.9453959,0.02485473,0.003497568,0.003633314,0.01509865,0.007519837],"domain_scores_gemma":[0.9399903,0.02523467,0.00341701,0.003033983,0.02239317,0.00593087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001085728,0.00006922314,0.00138708,0.000450438,0.00003372618,0.0004042428,0.02656697,0.005207296,0.0001324713,0.9297147,0.008587448,0.02743557],"study_design_scores_gemma":[0.0000299963,0.00005807518,0.002774934,0.004001625,0.00008333321,0.0002120838,0.06883181,0.008219409,0.0004757691,0.6369784,0.2781319,0.0002026391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01321298,0.01267483,0.2066133,0.3096556,0.0008124976,0.002250346,0.0009141868,0.0006299709,0.4532363],"genre_scores_gemma":[0.6843407,0.01632774,0.2537763,0.0135746,0.0003372139,0.002137176,0.0009105633,0.0003621511,0.02823357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6838793,"threshold_uncertainty_score":0.7932031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06401226925968094,"score_gpt":0.410175588956001,"score_spread":0.34616331969632,"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."}}