{"id":"W2086533598","doi":"10.1145/1982185.1982225","title":"Towards discovering criminal communities from textual data","year":2011,"lang":"en","type":"article","venue":"","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Law enforcement; Suspect; Computer science; Digital forensics; Closeness; Criminal investigation; Data science; Social network analysis; Unit (ring theory); World Wide Web; Information retrieval; Social media; Computer security; Criminology; Political science; Law; Sociology; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002321,0.001594979,0.001342967,0.03240041,0.002322076,0.003300092,0.001897935,0.002378897,0.001477959],"category_scores_gemma":[0.01436383,0.0008744735,0.001298206,0.01017996,0.001346219,0.004573866,0.003618151,0.001661076,0.002081713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000806776,"about_ca_system_score_gemma":0.00192532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00535193,"about_ca_topic_score_gemma":0.009551948,"domain_scores_codex":[0.9968129,0.0007248478,0.000311298,0.0008666792,0.001025559,0.0002586865],"domain_scores_gemma":[0.9890989,0.005061257,0.00184765,0.001154634,0.002332457,0.0005050646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008075595,0.001765052,0.06244644,0.002282972,0.000293401,0.002847401,0.006147102,0.01556122,0.05537664,0.01427028,0.01825381,0.819948],"study_design_scores_gemma":[0.0002079842,0.0003897588,0.0407044,0.0008930026,0.0004030172,0.004055077,0.01149417,0.7400553,0.0447642,0.08443928,0.07232233,0.0002714134],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887289,0.002918371,0.6766239,0.0018053,0.0001732882,0.002350274,0.01300584,0.004460069,0.009934109],"genre_scores_gemma":[0.2544323,0.0009193076,0.725966,0.000205901,0.0002191412,0.0007361211,0.01404357,0.0002414962,0.003236179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03240041,"threshold_uncertainty_score":0.0122748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1479071816936467,"score_gpt":0.2591536665290683,"score_spread":0.1112464848354217,"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."}}