{"id":"W2369616560","doi":"","title":"Personnel identification in mine underground based on maximin discriminant projection","year":2013,"lang":"en","type":"article","venue":"Meitan xuebao","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gait; Artificial intelligence; Minimax; Pattern recognition (psychology); Linear discriminant analysis; Subspace topology; Identification (biology); Pairwise comparison; Class (philosophy); Projection (relational algebra); Computer science; Fingerprint (computing); Biometrics; Mathematics; Computer vision; Data mining; Algorithm; Mathematical optimization","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":[0.000100383,0.0001163611,0.0001163989,0.0002738525,0.00004469422,0.00008605085,0.00006430191,0.00005174809,0.0003780207],"category_scores_gemma":[0.00001736956,0.0001082837,0.00005895337,0.0003048316,0.00001359094,0.0001834506,0.000004316541,0.00009918733,0.0005007447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354425,"about_ca_system_score_gemma":0.000006976372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002540053,"about_ca_topic_score_gemma":0.0005314748,"domain_scores_codex":[0.9992948,0.00002598256,0.0002000189,0.000171245,0.0001374926,0.0001704414],"domain_scores_gemma":[0.9997151,0.0000235249,0.00002591135,0.0001598222,0.00003139756,0.00004419238],"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":[0.0001290016,0.00286806,0.04113286,0.001715334,0.0003677727,0.00009633909,0.008522558,0.1291514,0.3677325,0.001952006,0.03743351,0.4088987],"study_design_scores_gemma":[0.0006006078,0.00004494074,0.06698602,0.00007994752,0.00003780549,0.000002817084,0.002902894,0.923324,0.004705496,0.0004460288,0.0005635468,0.0003058272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832442,0.0000362701,0.007453683,0.0003952523,0.000212529,0.0002601236,0.000004669865,0.0001651747,0.008228141],"genre_scores_gemma":[0.9982489,0.000009294438,0.0003180978,0.00005815908,0.00004213833,0.0001021347,0.00005267704,0.00002116859,0.001147386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7941727,"threshold_uncertainty_score":0.6436226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361449201515022,"score_gpt":0.2071090452918994,"score_spread":0.1934945532767492,"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."}}