{"id":"W2982490858","doi":"10.1111/tgis.12589","title":"A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data","year":2019,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario; Toronto Metropolitan University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Social media; Computer science; Event (particle physics); Outlier; Situation awareness; Data science; Similarity (geometry); Microblogging; Information retrieval; Geography; Data mining; World Wide Web; Artificial intelligence; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008763933,0.0006553246,0.0004677726,0.01087465,0.0006531081,0.001404582,0.0007348144,0.0007044439,0.0009770516],"category_scores_gemma":[0.002600862,0.0002252116,0.0008601741,0.006093431,0.0005055474,0.001745366,0.001149066,0.0004704381,0.0004997515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007094861,"about_ca_system_score_gemma":0.0008347975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00894073,"about_ca_topic_score_gemma":0.01372357,"domain_scores_codex":[0.9990565,0.0002102425,0.0001123833,0.0002255545,0.0003099973,0.00008526519],"domain_scores_gemma":[0.9986645,0.0004152867,0.000289133,0.0001703916,0.0003827936,0.00007785932],"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":[0.0007680213,0.001252739,0.1397479,0.0009650344,0.0006131869,0.001649878,0.002327167,0.122176,0.04749555,0.03771192,0.01145632,0.6338362],"study_design_scores_gemma":[0.00001900622,0.0001146867,0.03126537,0.00006768494,0.00009837044,0.0003457716,0.002838776,0.9335119,0.008134617,0.01417268,0.009370346,0.00006087351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1739626,0.0005381561,0.8122209,0.000657757,0.0001233613,0.0004254527,0.005064924,0.001935703,0.005071326],"genre_scores_gemma":[0.7568123,0.0002510539,0.2377243,0.00005739399,0.00009431425,0.0002096584,0.003685525,0.00004509144,0.001120303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01087465,"threshold_uncertainty_score":0.01777738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08490430918315241,"score_gpt":0.3417779730331456,"score_spread":0.2568736638499932,"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."}}