{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548464,0.001707493,0.001513794,0.003027691,0.0006789814,0.001980982,0.002100386,0.001724554,0.1504194],"category_scores_gemma":[0.01068332,0.0005284648,0.0008093725,0.004216555,0.0003128773,0.001171615,0.001594876,0.001253714,0.1448857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260079,"about_ca_system_score_gemma":0.002175435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01391311,"about_ca_topic_score_gemma":0.02502825,"domain_scores_codex":[0.9990354,0.0001589193,0.0001788144,0.0003001002,0.0001708291,0.0001560967],"domain_scores_gemma":[0.9954182,0.001532468,0.0004444605,0.0008430469,0.001305607,0.0004561837],"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":[0.00008027432,0.00003920063,0.001514866,0.0003161098,0.00001391706,0.000009831941,0.00001091194,0.00005428701,0.00004063851,0.0001126577,0.9948887,0.002918532],"study_design_scores_gemma":[0.0006583444,0.0001038044,0.02172337,0.0007827501,0.0000866777,0.0001222004,0.0001995941,0.0006101761,0.0004942594,0.001551854,0.9736148,0.00005207126],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002146301,0.00004774342,0.0000506547,0.00006101761,0.00002204964,0.00002535078,0.9988674,0.0001423815,0.0005688986],"genre_scores_gemma":[0.0009666544,0.00006217759,0.0001558025,0.00008944382,0.00001720214,0.0002728982,0.9964781,0.00005525814,0.001902419],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1504194,"threshold_uncertainty_score":0.5032029,"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."}}