{"id":"W4398293689","doi":"10.7910/dvn/f9thuh/h7ezzx","title":"Demographics and Post-Study Questionnaire.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anticipation (artificial intelligence); Perception; Demographics; Applied psychology; Psychology; Measure (data warehouse); Hazard; Virtual reality; Computer science; Human–computer interaction; Artificial intelligence; Data mining; Demography; Neuroscience","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004192379,0.0003756853,0.0004236064,0.0004803688,0.0001809079,0.0001341041,0.0004537786,0.0003978341,0.08750664],"category_scores_gemma":[0.0001817126,0.0003735045,0.0001125729,0.0001703357,0.0001056361,0.0002738814,0.0002528141,0.0007230551,0.2735817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004657591,"about_ca_system_score_gemma":0.00006427333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009001714,"about_ca_topic_score_gemma":0.0005196984,"domain_scores_codex":[0.9977392,0.0003947576,0.0005214886,0.0007181094,0.0003251358,0.000301302],"domain_scores_gemma":[0.9975338,0.0001307686,0.0003188248,0.001687708,0.0001669816,0.0001619119],"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":[0.0001135204,0.0004452776,0.0007509298,0.00006050048,0.0002558343,0.00008284806,0.0003639285,5.523489e-7,0.000001771078,0.0001665628,0.9973597,0.0003985888],"study_design_scores_gemma":[0.001039651,0.0002440767,0.02274167,0.00006772784,0.0002060712,0.00007611162,0.001053891,0.000009432795,1.348822e-7,0.000006913028,0.9741844,0.0003699074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001187251,0.000004342374,0.00002337267,0.00002244219,0.004649051,0.0007084263,0.9926202,0.0001164587,0.0006684618],"genre_scores_gemma":[0.001693312,0.00009567281,0.00002532551,0.001101137,0.0003697333,0.00006997403,0.9939743,0.00003203047,0.002638526],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.186075,"threshold_uncertainty_score":0.9998717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0235897959027809,"score_gpt":0.3329612414942519,"score_spread":0.309371445591471,"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."}}