{"id":"W2790488273","doi":"10.5072/zenodo.205009","title":"A Responsive User Body Suit (RUBS)","year":2017,"lang":"en","type":"article","venue":"New Interfaces for Musical Expression","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"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":[0.0007049462,0.0008599127,0.0006423652,0.0006049253,0.0005375074,0.0009774934,0.001366174,0.001573588,0.02511687],"category_scores_gemma":[0.001268785,0.0005247521,0.001011526,0.0003614396,0.0004313115,0.001058395,0.00246819,0.0007653966,0.009782495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140707,"about_ca_system_score_gemma":0.0002596851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002198427,"about_ca_topic_score_gemma":0.0002545434,"domain_scores_codex":[0.9992207,0.000114127,0.00004722271,0.000128633,0.0004162819,0.00007300282],"domain_scores_gemma":[0.9996468,0.0000808244,0.00003220465,0.00009836305,0.0000574156,0.00008440553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006814491,0.0002701556,0.001182741,0.000794829,0.00007390738,0.0009654423,0.0006702633,0.0008295066,0.580532,0.007272611,0.01844537,0.3882817],"study_design_scores_gemma":[0.000399269,0.004925639,0.01850307,0.0004390205,0.0004560374,0.01521437,0.0006516511,0.03060856,0.3487659,0.00525435,0.5743417,0.0004403876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137453,0.002268818,0.7661366,0.001076554,0.001555053,0.000913188,0.001203468,0.02148046,0.06791294],"genre_scores_gemma":[0.4956803,0.001713437,0.3248325,0.002408502,0.0004394565,0.000955902,0.001511056,0.003310365,0.1691484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02511687,"threshold_uncertainty_score":0.08402425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03693784834671823,"score_gpt":0.339268518456373,"score_spread":0.3023306701096548,"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."}}