{"id":"W4413474339","doi":"10.1049/csy2.70024","title":"Lightweight Hand Acupoint Recognition Based on Middle Finger Cun Measurement","year":2025,"lang":"en","type":"article","venue":"IET Cyber-Systems and Robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Petroleum Technology Research Centre; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Middle finger; Artificial intelligence; Pattern recognition (psychology); Medicine; Anatomy; Thumb","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009750798,0.0002977547,0.0004130849,0.0002812149,0.0003636535,0.0006368938,0.0003178917,0.0002044279,0.000005484575],"category_scores_gemma":[0.00008599148,0.0002335883,0.0001064149,0.0004617818,0.0000505463,0.0002066377,0.00009027339,0.0002170402,0.0001203761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001308096,"about_ca_system_score_gemma":0.0001540963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000666896,"about_ca_topic_score_gemma":0.00004917398,"domain_scores_codex":[0.9976001,0.0002409242,0.0005441263,0.000593268,0.0006520621,0.0003695346],"domain_scores_gemma":[0.9984269,0.0001752226,0.000183488,0.0005736343,0.0004771728,0.000163583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005789954,0.006254687,0.008920934,0.01020492,0.002498251,0.0007437376,0.01044843,0.1206369,0.02469358,0.1304528,0.2239015,0.4606652],"study_design_scores_gemma":[0.007102017,0.001104254,0.004173381,0.0125062,0.0002911924,0.0001317643,0.0003765646,0.7176539,0.01420157,0.00389121,0.2361314,0.002436517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003260321,0.0023688,0.9560369,0.005655956,0.006983075,0.001352208,0.00002902279,0.00034003,0.0239737],"genre_scores_gemma":[0.9903777,0.00005452363,0.006385989,0.0008991289,0.0003601526,0.0001096132,0.00001633667,0.00002392395,0.001772619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9871174,"threshold_uncertainty_score":0.9525456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04598322477190749,"score_gpt":0.2361923809145186,"score_spread":0.1902091561426111,"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."}}