{"id":"W4385938206","doi":"10.1109/access.2023.3306328","title":"Recognizing Emergencies and Multi-User Behavior Patterns Using Imperfect Data From Distributed Access Points. A Non-Intrusive Proof of Concept","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Polytechnique Montréal","keywords":"Computer science; Metadata; Proof of concept; Anomaly detection; Data mining; Information retrieval; Computer security; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006883137,0.0001543106,0.0003190473,0.0001384389,0.000530928,0.0002911136,0.001271276,0.0001128459,0.0005869933],"category_scores_gemma":[0.0004321966,0.0001521419,0.00007033155,0.0009182348,0.0003087453,0.001504035,0.0004377906,0.000137231,0.000006148593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000654211,"about_ca_system_score_gemma":0.000206672,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1092804,"about_ca_topic_score_gemma":0.08013467,"domain_scores_codex":[0.998144,0.0002411863,0.0004022116,0.000532706,0.0003575741,0.00032229],"domain_scores_gemma":[0.998521,0.0003041915,0.0002488684,0.0005487314,0.0002574995,0.0001196506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002848496,0.0002938117,0.9587673,0.0001005524,0.0002357633,0.00002130302,0.01600533,0.0007674864,0.002624251,0.0000137346,0.001093531,0.02004847],"study_design_scores_gemma":[0.002414941,0.00009058649,0.7772521,0.0006163465,0.001778638,9.715899e-7,0.03452711,0.1011695,0.07868636,0.0005080699,0.00132802,0.001627362],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802345,0.00006036651,0.01656267,0.0001785826,0.0003005335,0.0004810663,0.002105142,0.00006152762,0.00001562185],"genre_scores_gemma":[0.9986933,0.0000791951,0.0000823475,0.00004942684,0.0001893922,0.00004272696,0.0008060679,0.00001540434,0.00004211085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1815151,"threshold_uncertainty_score":0.9366505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1857270887423473,"score_gpt":0.426556329129859,"score_spread":0.2408292403875117,"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."}}