{"id":"W1595341331","doi":"","title":"디지털 인체모델링 도구를 이용한 한국인 휠체어 사용자 기본치수 설정에 관한 연구","year":2006,"lang":"ko","type":"article","venue":"대한건축학회 논문집 - 계획계","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anthropometry; Wheelchair; Dimension (graph theory); Computer science; Measure (data warehouse); Geography; Mathematics; Data mining; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005829856,0.0004538439,0.0002808697,0.001421728,0.0004884569,0.0006010847,0.0004318641,0.0001856035,0.005236265],"category_scores_gemma":[0.001747451,0.0001725505,0.0003520783,0.00164265,0.0003202949,0.0007721139,0.0005456252,0.0002370874,0.001713609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00026531,"about_ca_system_score_gemma":0.0005423757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005675745,"about_ca_topic_score_gemma":0.01291563,"domain_scores_codex":[0.9996126,0.00006173976,0.00009216848,0.00008894273,0.0001046855,0.0000399006],"domain_scores_gemma":[0.9988477,0.0001695387,0.0003093281,0.000129102,0.0004735046,0.00007074357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003768091,0.0002988956,0.6015676,0.001051694,0.0001192338,0.0003111558,0.002670326,0.0005995283,0.01520477,0.001164458,0.004310655,0.3723249],"study_design_scores_gemma":[0.00001275959,0.0005016576,0.9694558,0.0001376351,0.0001133739,0.0007896263,0.004562316,0.0004966418,0.004994651,0.0004869035,0.01839545,0.00005314134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9662506,0.001335253,0.01179322,0.0003840456,0.0001239836,0.0002046994,0.004492451,0.0001266288,0.01528914],"genre_scores_gemma":[0.9721706,0.00194813,0.01759109,0.0001967582,0.00003816551,0.000447395,0.003479546,0.0000381773,0.004090154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005675745,"threshold_uncertainty_score":0.01751709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008598613959718655,"score_gpt":0.2706593837585294,"score_spread":0.2620607697988107,"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."}}