{"id":"W4404572650","doi":"10.2139/ssrn.5029273","title":"Extracting Information from Reddit for Emergency Management - a Case Study on British Columbia Wildfire","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Emergency management; Geography; History; Environmental resource management; Political 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002185774,0.0002387969,0.0003420829,0.000225298,0.000555199,0.003246041,0.001209365,0.0001358918,0.0000225764],"category_scores_gemma":[0.00006062231,0.0003308594,0.0003210537,0.0003539092,0.000009688494,0.0006434874,0.001024274,0.003137861,0.00003931851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008243957,"about_ca_system_score_gemma":0.0008492228,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02486086,"about_ca_topic_score_gemma":0.05936795,"domain_scores_codex":[0.9965814,0.0001122543,0.0008027636,0.0006092346,0.0005307342,0.001363657],"domain_scores_gemma":[0.9984852,0.00005588836,0.0005478437,0.000718491,0.00009244674,0.0001001136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001028014,0.0002830124,0.0006980597,0.00007481438,0.002317908,0.001511749,0.001888339,0.0002645889,0.000001009506,0.001179489,0.01001603,0.9817547],"study_design_scores_gemma":[0.002471903,0.002241681,0.001613743,0.001502815,0.00243589,0.01016879,0.06134213,0.0813276,0.000002417251,0.8239047,0.01084663,0.002141693],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7598029,0.001659437,0.2338247,0.0004298861,0.002494684,0.0008708306,0.0001601214,0.0002291552,0.0005282981],"genre_scores_gemma":[0.9933515,0.001038129,0.003284929,0.00004204016,0.0005652877,0.0001210182,0.0001157901,0.00002826738,0.001452998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.979613,"threshold_uncertainty_score":0.9999143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334325022238683,"score_gpt":0.2681041399708129,"score_spread":0.2547608897484261,"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."}}