{"id":"W4293551514","doi":"","title":"SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Digital Accessibility for Disabilities","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Assistive technology; Computer science; Speech recognition; Human–computer interaction","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":[0.00775451,0.002574503,0.002921256,0.001822972,0.001416126,0.005900255,0.003286254,0.002913674,0.03654257],"category_scores_gemma":[0.006603653,0.000780585,0.002077561,0.001506174,0.001479482,0.004987596,0.006701349,0.003642427,0.01890037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312351,"about_ca_system_score_gemma":0.004956473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003031565,"about_ca_topic_score_gemma":0.004283674,"domain_scores_codex":[0.996298,0.001285798,0.0002940136,0.0008262857,0.0008777833,0.0004180085],"domain_scores_gemma":[0.9956059,0.001157351,0.00008066291,0.000860282,0.001483013,0.0008127606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001868938,0.0009320943,0.001396594,0.001951476,0.0003522392,0.0006017867,0.002274167,0.007597223,0.04785732,0.02164522,0.2648261,0.6486969],"study_design_scores_gemma":[0.0002115518,0.001289527,0.004973676,0.0009696269,0.0003617557,0.0009443749,0.001399737,0.05923117,0.06986283,0.04287268,0.8177076,0.0001756333],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.03663196,0.02554099,0.8343224,0.00665754,0.01856172,0.001072752,0.004397983,0.01066909,0.06214553],"genre_scores_gemma":[0.2431246,0.01469357,0.3913206,0.002869087,0.007361806,0.001543633,0.02954674,0.006085686,0.3034542],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03654257,"threshold_uncertainty_score":0.122247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395030435659022,"score_gpt":0.3188606489637678,"score_spread":0.2849103446071775,"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."}}