{"id":"W4200077347","doi":"10.32920/17114147","title":"Lingo-Entrainment: The Natural Language Surveillance Of Smartphone Users","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Digital Communication and Language","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; York University; University of Toronto","funders":"","keywords":"Context (archaeology); Entrainment (biomusicology); Divestment; Internet privacy; Psychology; Advertising; Computer science; Business; Political science; Aesthetics; History; Law; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003900599,0.0001681508,0.0002388435,0.00004588068,0.00005123238,0.0003814997,0.002734401,0.00007952132,0.00007044555],"category_scores_gemma":[0.00009359804,0.0001145287,0.0001684516,0.000232014,0.00007847721,0.0001387123,0.00312041,0.0004590409,0.00001159635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003057041,"about_ca_system_score_gemma":0.0001194853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002712639,"about_ca_topic_score_gemma":0.0003015843,"domain_scores_codex":[0.9987102,0.0001669997,0.0002858165,0.0003229486,0.0003282515,0.0001858489],"domain_scores_gemma":[0.9971581,0.0001939662,0.0001907462,0.00231759,0.00009245082,0.0000472036],"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":[0.00005180101,0.001463545,0.004265338,0.001107881,0.001330048,0.0002731384,0.1207478,0.001290984,0.01121975,0.3259221,0.01032635,0.5220013],"study_design_scores_gemma":[0.007192385,0.0004397449,0.262536,0.002889564,0.0001685798,0.0003425479,0.0640327,0.2960494,0.1613033,0.005295521,0.1894591,0.0102912],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.396042,0.02987326,0.05669859,0.009456298,0.002371268,0.0009756577,0.00005382358,0.0007989916,0.5037301],"genre_scores_gemma":[0.9915492,0.0001103195,0.005770557,0.001022583,0.00002116879,0.00001071723,0.0000572952,0.000009046837,0.001449141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5955071,"threshold_uncertainty_score":0.5081243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128656211749104,"score_gpt":0.2495936191490264,"score_spread":0.2383070570315353,"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."}}