{"id":"W4294891725","doi":"10.1145/3533387","title":"Dissecting My Data Body","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Computer Graphics and Interactive Techniques","topic":"Empathy and Medical Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"The arts; Virtual reality; Data science; Social media; Big data; Computer science; Internet privacy; Sociology; Engineering ethics; World Wide Web; Human–computer interaction; Engineering; Visual arts; Art","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004145924,0.00009735537,0.0001650757,0.000126573,0.0001778706,0.00002050031,0.0007586514,0.00003086034,0.00001491114],"category_scores_gemma":[0.0006275389,0.00006771509,0.00005300945,0.0002300127,0.00008566777,0.0001520447,0.002024883,0.0005221555,1.389645e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002903151,"about_ca_system_score_gemma":0.00002277885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000032571,"about_ca_topic_score_gemma":3.158927e-7,"domain_scores_codex":[0.9991893,0.00000812018,0.0001789402,0.0002652578,0.0002524946,0.0001059313],"domain_scores_gemma":[0.9990444,0.0001089361,0.0001774566,0.0004110519,0.0002048382,0.00005331586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003092308,0.006459237,0.1385797,0.001721513,0.001080758,0.00001254017,0.06222263,3.770846e-7,0.08548258,0.1971138,0.2866052,0.2176294],"study_design_scores_gemma":[0.002215299,0.01175176,0.1963735,0.003838963,0.0006214626,0.0008305507,0.02812879,0.0140314,0.4724228,0.1160483,0.1525843,0.001152821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931204,0.00004904869,0.00008908333,0.004496488,0.000318133,0.000361174,0.00001283614,0.00008546187,0.00146742],"genre_scores_gemma":[0.9946768,0.0000594502,0.003373238,0.001557615,0.0001943288,0.00003658957,0.00001067137,0.00001184215,0.00007941569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3869402,"threshold_uncertainty_score":0.2761341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522986588336567,"score_gpt":0.3324493206405801,"score_spread":0.2972194547572145,"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."}}