{"id":"W4301391789","doi":"10.1109/tvcg.2022.3209365","title":"KiriPhys: Exploring New Data Physicalization Opportunities","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"","keywords":"Curiosity; Computer science; Data exploration; Data science; Human–computer interaction; Data visualization; Qualitative property; Visualization; World Wide Web; Artificial intelligence; Machine learning; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002227883,0.0001923216,0.0001624634,0.0006609835,0.0008450833,0.0001904317,0.0008169399,0.00005027169,0.00004337248],"category_scores_gemma":[0.000002530836,0.0002239545,0.00004275754,0.00117873,0.00007249317,0.00155506,0.00005948813,0.0003331676,0.00000567935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005239031,"about_ca_system_score_gemma":0.00007866957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002237767,"about_ca_topic_score_gemma":0.000008061786,"domain_scores_codex":[0.9983723,0.000168808,0.0003032757,0.0005713519,0.0003859253,0.0001984026],"domain_scores_gemma":[0.9988317,0.00007170845,0.0001427003,0.0007621822,0.000121073,0.00007063252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001119393,0.0002069245,0.000009418233,0.00001056041,0.00004660209,0.000005240118,0.001045796,0.001113679,0.00003771085,0.9608193,0.002103892,0.03458971],"study_design_scores_gemma":[0.0003974854,0.000298488,0.00006097794,0.00001788498,0.00001724267,0.0000319242,0.0002206337,0.9713503,0.001469007,0.003880996,0.02198158,0.0002734736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005001127,0.0000191908,0.992181,0.0004675471,0.001522363,0.0001783578,0.00002120551,0.0005502906,0.00005888682],"genre_scores_gemma":[0.9929073,0.0002169815,0.003092722,0.003144657,0.0001425788,0.0001025951,0.0001215766,0.0000360064,0.0002355435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9890883,"threshold_uncertainty_score":0.9132599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2220589424088418,"score_gpt":0.324794519415448,"score_spread":0.1027355770066062,"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."}}