{"id":"W2498732389","doi":"10.1017/cbo9781316050835.008","title":"Tar and Feathers","year":2015,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Historical and Literary Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Feather; tar (computing); Art; Zoology; Biology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446257,0.0006104995,0.0002962716,0.001185066,0.002290254,0.003158024,0.0004042989,0.001030246,0.1094686],"category_scores_gemma":[0.001192616,0.0002238419,0.0003556518,0.000865188,0.002016583,0.003320398,0.001467036,0.002366329,0.03434904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476778,"about_ca_system_score_gemma":0.0009914426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005031793,"about_ca_topic_score_gemma":0.0115311,"domain_scores_codex":[0.9997422,0.00005760867,0.000007156362,0.00004731715,0.000106953,0.00003879044],"domain_scores_gemma":[0.9997625,0.00008386181,0.00001140375,0.00003296973,0.00007003462,0.00003919855],"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":[0.00003237946,0.00002203745,0.0001332756,0.0001579207,0.000004185375,0.0001166035,0.00243884,0.00008346755,0.0002968991,0.3429874,0.5400308,0.1136962],"study_design_scores_gemma":[0.000001055589,0.000003756949,0.00009992019,0.0001205562,9.100074e-7,0.00005373537,0.0002962082,0.00001911085,0.0000577276,0.007297501,0.9920476,0.0000018089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001010682,0.02182558,0.001229191,0.007074301,0.004678845,0.00002076574,0.000116343,0.00008602189,0.9639584],"genre_scores_gemma":[0.00917955,0.005892146,0.0004228844,0.00138733,0.0005369093,0.0000115282,0.00005663988,0.0001127559,0.9824002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1094686,"threshold_uncertainty_score":0.3662091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433211519766151,"score_gpt":0.2271424240247036,"score_spread":0.1828103088270421,"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."}}