{"id":"W2571826164","doi":"10.1109/wi.2016.0034","title":"Detecting the Magnitude of Events from News Articles","year":2016,"lang":"en","type":"article","venue":"","topic":"Terrorism, Counterterrorism, and Political Violence","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada; Georgetown University","keywords":"Computer science; Event (particle physics); Vocabulary; Similarity (geometry); Set (abstract data type); Focus (optics); Magnitude (astronomy); Natural language processing; Correlation; Word (group theory); Artificial intelligence; Information retrieval; Data science; Data mining; Machine learning; Mathematics; Linguistics; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511203,0.0010732,0.0008063963,0.01506581,0.0005376588,0.002115789,0.0007997417,0.001025318,0.001115686],"category_scores_gemma":[0.009217139,0.0003622789,0.000736788,0.005863723,0.0003305376,0.002507219,0.001058843,0.0008557049,0.001017668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005231617,"about_ca_system_score_gemma":0.000554365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004331161,"about_ca_topic_score_gemma":0.007976045,"domain_scores_codex":[0.9981923,0.0002396804,0.0002996894,0.0004376491,0.0007219916,0.0001087262],"domain_scores_gemma":[0.9902283,0.004232844,0.002378633,0.0004593807,0.002361489,0.0003394324],"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":[0.0007393145,0.0006280671,0.3738047,0.002173296,0.0006535437,0.002857918,0.002187417,0.01616823,0.04139634,0.002697047,0.01940783,0.5372863],"study_design_scores_gemma":[0.00005694921,0.0005936492,0.6601975,0.0003431197,0.0005806698,0.002753335,0.004246877,0.253084,0.03047269,0.005985441,0.04151008,0.0001756796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8056989,0.005938041,0.1406239,0.001674443,0.00081037,0.0009312095,0.02668739,0.004902064,0.01273372],"genre_scores_gemma":[0.8806542,0.001967788,0.09283216,0.0001162973,0.0007522443,0.000294355,0.01983358,0.0001169757,0.003432526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01506581,"threshold_uncertainty_score":0.008611917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268186895528836,"score_gpt":0.3117178049296883,"score_spread":0.2790359359743999,"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."}}