{"id":"W7117456232","doi":"10.1145/3714394.3756207","title":"Summary of SHL Challenge 2025: Locomotion and Transportation Mode Recognition Using Foundation Models","year":2025,"lang":"","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Foundation (evidence); Software; Mode (computer interface); Motion (physics); Protocol (science)","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":[0.02026983,0.008728634,0.005504305,0.004530687,0.003114543,0.006441034,0.006502036,0.005668906,0.02356797],"category_scores_gemma":[0.03508866,0.001150744,0.004458907,0.003183031,0.001240127,0.00692519,0.007820834,0.004787634,0.03590404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003168766,"about_ca_system_score_gemma":0.005553747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0231339,"about_ca_topic_score_gemma":0.02637328,"domain_scores_codex":[0.981253,0.005292261,0.001386338,0.003442091,0.006808091,0.001818171],"domain_scores_gemma":[0.9712199,0.005315241,0.0005427984,0.003625613,0.01487339,0.004423095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000745858,0.0005022881,0.001367919,0.002254767,0.0004401479,0.0002856662,0.0001563068,0.002649798,0.003560936,0.0005411687,0.8765627,0.1109325],"study_design_scores_gemma":[0.001095294,0.003295217,0.01753644,0.002330699,0.000908806,0.002412653,0.002025075,0.06801821,0.03161131,0.007508914,0.8625943,0.0006631303],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1091428,0.07510184,0.1710955,0.03143775,0.08978561,0.009340516,0.3136666,0.1100451,0.09038436],"genre_scores_gemma":[0.0591323,0.005859906,0.0746169,0.005232295,0.003961031,0.002873865,0.7818266,0.006720087,0.05977696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02356797,"threshold_uncertainty_score":0.1071984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04708417523300174,"score_gpt":0.2716425851545006,"score_spread":0.2245584099214988,"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."}}