{"id":"W4242663261","doi":"10.32920/ryerson.14645703","title":"Everyone left","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Toronto Metropolitan University","funders":"","keywords":"Procurement; Government (linguistics); Business; Public relations; Marketing; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001742697,0.0001247515,0.0001576316,0.00004525794,0.00001076873,0.00005810903,0.0001589267,0.0002517121,0.0008186232],"category_scores_gemma":[0.0000103152,0.0001217391,0.00006793391,0.00002483315,0.00001566616,0.00002123437,0.0004016438,0.0003372457,0.00003546256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003186343,"about_ca_system_score_gemma":0.0000115671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000295345,"about_ca_topic_score_gemma":0.00005114674,"domain_scores_codex":[0.9995796,0.000002094648,0.00009659092,0.000127544,0.00006431052,0.0001298595],"domain_scores_gemma":[0.9995835,0.00001189336,0.000007101745,0.0003660542,0.00001607397,0.00001534294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003714404,0.00007556284,0.006976272,0.003613088,0.001572508,0.0003776878,0.001293307,0.2733262,0.003560747,0.008571262,0.6331981,0.06743159],"study_design_scores_gemma":[0.0007246325,0.00005460458,0.02697988,0.001147458,0.0001984194,0.0001277303,0.008009739,0.09476868,0.2751417,0.01830912,0.5696735,0.004864561],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6516797,0.01273485,0.04864299,0.0002704899,0.006232132,0.0002292329,0.00002170343,0.01139055,0.2687984],"genre_scores_gemma":[0.9915329,0.00107181,0.00425247,0.00003340484,0.00009399012,0.00001119499,0.00003203345,0.00003000673,0.00294219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3398532,"threshold_uncertainty_score":0.896335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287091413538036,"score_gpt":0.19742969408879,"score_spread":0.1845587799534097,"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."}}