{"id":"W2262976093","doi":"","title":"Designing Intelligent Wheelchairs: Reintegrating AI","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Wheelchair; Engineering management; Human–computer interaction; Artificial intelligence; Knowledge management; Engineering; World Wide Web","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":[0.001571335,0.0007751487,0.0004039719,0.0005826112,0.000639536,0.002370564,0.001219959,0.001469071,0.003165025],"category_scores_gemma":[0.002761005,0.0004803368,0.0004712844,0.0003024182,0.001981832,0.003379078,0.002248112,0.001173733,0.0009844356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005363625,"about_ca_system_score_gemma":0.001110206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605668,"about_ca_topic_score_gemma":0.002445815,"domain_scores_codex":[0.9991407,0.0003688209,0.0000626554,0.0001303034,0.0002266686,0.00007075852],"domain_scores_gemma":[0.9992104,0.000400661,0.00004781218,0.000129133,0.0001537078,0.00005836239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008917078,0.0002293078,0.001869658,0.001256778,0.0001121481,0.0003876159,0.004162993,0.08354877,0.04744952,0.3073762,0.009125343,0.5443925],"study_design_scores_gemma":[0.00007465931,0.0004695952,0.001302985,0.0006772649,0.0001653384,0.0006356729,0.001825499,0.3393675,0.0312095,0.2212417,0.4028917,0.0001386291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01320308,0.002127136,0.9626752,0.001724071,0.0002140249,0.0001739708,0.00003103958,0.0006908617,0.01916065],"genre_scores_gemma":[0.1634958,0.003590625,0.8206068,0.0004286414,0.0001072582,0.000259593,0.00009243723,0.0002440533,0.01117474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003165025,"threshold_uncertainty_score":0.01058811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1180783750157088,"score_gpt":0.3369628519752738,"score_spread":0.218884476959565,"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."}}