{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004161357,0.0004541083,0.0006441223,0.001003551,0.0003678674,0.0003689113,0.000366793,0.0003141647,0.0006614172],"category_scores_gemma":[0.0007708575,0.0002153798,0.0003848734,0.0008765954,0.0003184528,0.000555319,0.0004654712,0.0004014916,0.0002390993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180673,"about_ca_system_score_gemma":0.0005069635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00198707,"about_ca_topic_score_gemma":0.001614484,"domain_scores_codex":[0.9996881,0.00007606676,0.00001407829,0.00007436387,0.0001176789,0.00002977195],"domain_scores_gemma":[0.9998295,0.00004911067,0.00002328461,0.00001522816,0.0000662308,0.00001660506],"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":[0.0004738749,0.0002190105,0.01016816,0.0001551858,0.00009659803,0.0002927743,0.0003355253,0.1213346,0.06842295,0.005961335,0.00296781,0.7895721],"study_design_scores_gemma":[0.00001396947,0.00007695456,0.003966971,0.000006488575,0.00001532028,0.0001883172,0.00007507647,0.9853591,0.007761273,0.001802901,0.0007054474,0.00002817597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1555184,0.0001574927,0.8428622,0.0001045316,0.00003035047,0.00003500845,0.00005107129,0.0002798595,0.0009610518],"genre_scores_gemma":[0.7196364,0.0002831181,0.2772613,0.00004276556,0.000037098,0.00007394499,0.0001808201,0.00003604114,0.002448444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00198707,"threshold_uncertainty_score":0.003950953,"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."}}