{"id":"W4401591794","doi":"10.1038/s41597-024-03683-5","title":"The NACOB multi-surface walking dataset","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; McGill University","funders":"Fonds de Recherche du Québec - Santé; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Information retrieval; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008636766,0.00007227664,0.0000539496,0.00004609241,0.0002691889,0.001347777,0.0008323317,0.00001890449,0.0002580894],"category_scores_gemma":[0.00004825727,0.00004968208,0.00002341737,0.0005433802,0.00008385552,0.0003377054,0.00026053,0.0001098189,0.002217179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001332212,"about_ca_system_score_gemma":0.00001536019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000725389,"about_ca_topic_score_gemma":0.0001349952,"domain_scores_codex":[0.9991756,0.00001796779,0.0001226714,0.0003236425,0.0001752835,0.0001848378],"domain_scores_gemma":[0.9987061,0.00006188834,0.000006817384,0.001161147,0.00001443431,0.00004963719],"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":[1.915227e-7,0.000004856408,0.000005143643,0.00002592563,0.00004123708,0.000007037656,0.00004417879,0.0003819982,0.002913581,0.00005195488,0.9454807,0.05104321],"study_design_scores_gemma":[0.0000204694,3.696466e-7,0.00001707421,0.00001565179,0.00001672473,0.000001626396,0.00006145277,0.3616391,0.0003006006,0.00003056831,0.6378461,0.00005027697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06153148,0.05541435,0.1177973,0.004049381,0.06867416,0.001168686,0.6661171,0.006864503,0.01838303],"genre_scores_gemma":[0.7592549,0.0005544433,0.007332694,0.0001168573,0.0003461693,0.00001023873,0.2087754,0.0000884745,0.02352081],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.6977234,"threshold_uncertainty_score":0.9996889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06017147036614638,"score_gpt":0.2995975986565877,"score_spread":0.2394261282904414,"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."}}