{"id":"W2163924061","doi":"10.1109/wfopc.2011.6089679","title":"Dataglove for consumer applications","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Computer science; Sensory system; Human–computer interaction; Interface (matter); Degrees of freedom (physics and chemistry); Human–machine interface; Computer hardware; Embedded system; Human–machine system; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006118553,0.001282186,0.000625937,0.0008814835,0.0003680912,0.0009728387,0.001650328,0.001446137,0.2145912],"category_scores_gemma":[0.001682927,0.000351046,0.0006047935,0.0008379495,0.0002946749,0.001664686,0.001959558,0.001103159,0.08484428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002594953,"about_ca_system_score_gemma":0.0003203462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004036807,"about_ca_topic_score_gemma":0.0005568422,"domain_scores_codex":[0.9995083,0.00005413014,0.00002662675,0.00008622331,0.0002761278,0.00004872268],"domain_scores_gemma":[0.9990968,0.0001702353,0.00003831432,0.000369905,0.0002519237,0.00007291626],"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":[0.001337445,0.0002186332,0.001361788,0.001158053,0.00006684955,0.000552592,0.0003018467,0.0006910439,0.1020116,0.009471929,0.2850687,0.5977596],"study_design_scores_gemma":[0.0002547265,0.0004345984,0.002621829,0.0003047593,0.00006462268,0.001291842,0.00009990847,0.006945626,0.0627057,0.006148813,0.9190169,0.0001106799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02009666,0.005899049,0.5685697,0.002612268,0.001246321,0.001493172,0.01719926,0.1638572,0.2190264],"genre_scores_gemma":[0.2327155,0.004082759,0.2418567,0.004273886,0.0005361618,0.00188738,0.02971704,0.02182544,0.4631052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2145912,"threshold_uncertainty_score":0.7178791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08539540114504245,"score_gpt":0.2724629577508578,"score_spread":0.1870675566058153,"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."}}