{"id":"W3035319564","doi":"10.3390/proceedings2020049044","title":"Development of Customised Wheelchair Racing Gloves Using Digital Fabrication Techniques","year":2020,"lang":"en","type":"article","venue":"","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Wheelchair; 3d printed; 3D printing; Engineering; Automotive engineering; Computer science; Mechanical engineering; Engineering drawing; Manufacturing engineering; Simulation","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":[0.00003220091,0.00006140578,0.00008266072,0.00003063494,0.00001972267,0.00001803116,0.00005097252,0.00003040433,0.000009095213],"category_scores_gemma":[0.00002622848,0.00005604246,0.00002270928,0.0001037661,0.00001443586,0.00008925219,0.00001958393,0.00003457139,0.000005130677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008632,"about_ca_system_score_gemma":0.00001930177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.321656e-7,"about_ca_topic_score_gemma":1.448742e-7,"domain_scores_codex":[0.9995791,0.000002365867,0.0002034111,0.00006740886,0.00007808613,0.00006957055],"domain_scores_gemma":[0.9998162,0.00001334123,0.00002461653,0.0000577776,0.0000493726,0.00003863752],"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":[0.000009216738,0.00004353526,0.0008126742,0.0004219962,0.00005001965,5.014076e-7,0.007885042,0.08514184,0.7900175,0.001334243,0.0001752171,0.1141082],"study_design_scores_gemma":[0.0001637752,0.00002390219,0.0005436008,0.00004826267,0.000006570688,7.565877e-7,0.0005838704,0.428727,0.5644512,0.0001285078,0.005102699,0.0002198171],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2214044,0.00003485495,0.7744685,0.00009008011,0.00003462293,0.0001535366,0.000001933758,0.0002942086,0.003517863],"genre_scores_gemma":[0.7813331,0.000001707401,0.2186109,0.00001685446,0.00001343705,0.000002296148,0.000002621579,0.000009848525,0.000009255506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5599287,"threshold_uncertainty_score":0.2285345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002656271169495,"score_gpt":0.2250778910872332,"score_spread":0.2050513283755383,"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."}}