{"id":"W4225887541","doi":"10.1007/s11069-021-05081-1","title":"Early detection of emergency events from social media: a new text clustering approach","year":2022,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"GreenField Specialty Alcolhols (Canada)","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Cluster analysis; Event (particle physics); Computer science; Social media; Big data; Data mining; Emergency management; Similarity (geometry); Natural disaster; Data science; Artificial intelligence; World Wide Web; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001432765,0.0001396976,0.0003072074,0.00009612991,0.0001355733,0.000004132381,0.0001823029,0.00006482948,0.0006724955],"category_scores_gemma":[0.0001473268,0.0001376015,0.0001798734,0.0003634572,0.00001921972,0.00009514827,0.0002361225,0.0004357047,0.00001745828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625268,"about_ca_system_score_gemma":0.0001218725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000383004,"about_ca_topic_score_gemma":0.00009782967,"domain_scores_codex":[0.998392,0.00009569745,0.0002979038,0.0002980891,0.0006996277,0.0002166726],"domain_scores_gemma":[0.9994059,0.00002976258,0.0001431707,0.0002337496,0.00007322481,0.0001141463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00556698,0.0008162378,0.03600565,0.0002882813,0.0008218315,0.00007385851,0.006584467,0.00006610523,0.09316872,0.00003681491,0.02180751,0.8347635],"study_design_scores_gemma":[0.00346988,0.0002194645,0.9845256,0.00002121878,0.0001821131,0.00001671917,0.0005415964,0.003993671,0.0008089344,0.0002624438,0.00563536,0.0003230358],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950162,0.001120678,0.0004174664,0.0001517205,0.001904823,0.0002609863,0.0003996485,0.0001040963,0.0006243633],"genre_scores_gemma":[0.9977963,0.00001378444,0.0005420067,0.00005734614,0.0007081273,0.00002546397,0.0004305696,0.00002656148,0.0003998249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9485199,"threshold_uncertainty_score":0.7363354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919630429184238,"score_gpt":0.2747548002183597,"score_spread":0.2555584959265174,"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."}}