{"id":"W7111690098","doi":"","title":"Machine Learning for Tangible Effects: Natural Language Processing for Uncovering the Illicit Massage Industry & Computer Vision for Tactile Sensing","year":2023,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Acronym; Word2vec; Intension; Liberian dollar; Identity theft; Database transaction; Work (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530328,0.001275499,0.0006113875,0.002348154,0.0007261363,0.002249167,0.0008603053,0.001304393,0.004961268],"category_scores_gemma":[0.00530772,0.000329857,0.001736112,0.001771368,0.000995828,0.003445013,0.00152916,0.002620561,0.002983584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007543887,"about_ca_system_score_gemma":0.0008700445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003811737,"about_ca_topic_score_gemma":0.005041705,"domain_scores_codex":[0.9989172,0.0002992152,0.00009679028,0.000327979,0.0002565014,0.0001022455],"domain_scores_gemma":[0.9978516,0.001438077,0.0002038539,0.0002060042,0.0002432632,0.00005719696],"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":[0.0002719198,0.0004144218,0.006177316,0.0008773684,0.0001360741,0.0005744696,0.0004933993,0.02190641,0.02286744,0.01716079,0.06209116,0.8670292],"study_design_scores_gemma":[0.00006844021,0.0003377625,0.009385269,0.0002935034,0.00009810996,0.0004419483,0.001111449,0.7929655,0.01648455,0.104517,0.07415593,0.000140463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05881207,0.00735583,0.8900166,0.009512459,0.001990079,0.0005603216,0.01020514,0.01065614,0.01089129],"genre_scores_gemma":[0.2819005,0.004548545,0.6810141,0.002267514,0.001164617,0.00126027,0.01657896,0.0005479401,0.01071752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004961268,"threshold_uncertainty_score":0.01659715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01888631675923937,"score_gpt":0.2710825338963371,"score_spread":0.2521962171370977,"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."}}