{"id":"W4410065062","doi":"10.1016/j.ijdrr.2025.105503","title":"HurricaneLog: A serious game for data collection and analysis of hurricane preparedness and response operations","year":2025,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Preparedness; Data collection; Hurricane katrina; Disaster preparedness; Disaster response; Emergency management; Aeronautics; Operations research; Computer security; Engineering; Computer science; Medical emergency; Operations management; Natural disaster; Geography; Medicine; Meteorology; Political science; Sociology; Economics; Management","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":[0.0006384487,0.00007992529,0.0001799724,0.0009659646,0.00007692089,0.0001309381,0.0002184092,0.00002794752,0.00002749179],"category_scores_gemma":[0.0004035769,0.00007456235,0.00005999313,0.0005931997,0.00006352724,0.0008460989,0.0001368808,0.00005670626,5.528113e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008337804,"about_ca_system_score_gemma":0.00006363669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009573147,"about_ca_topic_score_gemma":0.0004398179,"domain_scores_codex":[0.9990712,0.00002880137,0.0004562514,0.0001786037,0.0002005694,0.00006461154],"domain_scores_gemma":[0.99893,0.00002830923,0.0002275853,0.000160587,0.0006430588,0.00001046385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02971741,0.003585732,0.1211232,0.001062744,0.03251153,0.00001436787,0.02195414,0.293156,0.02053767,0.04532942,0.02040383,0.4106039],"study_design_scores_gemma":[0.005023703,0.0002078862,0.2641772,0.0002660414,0.006906278,0.00003261586,0.01644644,0.675205,0.0001898097,0.004400579,0.02670112,0.0004433488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594842,0.0001071726,0.03726885,0.001867472,0.0009428624,0.000189078,0.00003605964,0.000008689517,0.00009560544],"genre_scores_gemma":[0.9985701,0.0001381318,0.0007672647,0.00007851694,0.000127324,0.000009711902,0.00007919564,0.00000426857,0.000225433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4101606,"threshold_uncertainty_score":0.3040564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364880759863735,"score_gpt":0.3125476458730899,"score_spread":0.2888988382744525,"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."}}