{"id":"W4387544966","doi":"10.2139/ssrn.4598768","title":"An Innovative Unsupervised Gait Recognition Based Tracking System for Safeguarding Large-Scale Nature Reserves in Complex Terrain","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Safeguarding; Terrain; Gait; Scale (ratio); Tracking (education); Computer science; Artificial intelligence; Physical medicine and rehabilitation; Geography; Psychology; Cartography; Medicine","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003581826,0.0004665861,0.0007230953,0.001229797,0.0002333409,0.0002741069,0.0004754864,0.0006826913,0.0000368988],"category_scores_gemma":[0.0001157218,0.0004947935,0.0003742611,0.0009758737,0.00002164209,0.0002679728,0.00005543792,0.00634306,0.00001507001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002171611,"about_ca_system_score_gemma":0.0006935988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004615068,"about_ca_topic_score_gemma":0.004416346,"domain_scores_codex":[0.9958078,0.000274249,0.0008901738,0.0005158292,0.000367869,0.00214407],"domain_scores_gemma":[0.9987013,0.000129665,0.0002542281,0.0003000885,0.0005026309,0.0001121133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001618795,0.001603026,0.01702421,0.01452271,0.009761301,0.0002526082,0.01375603,0.5959554,0.06096167,0.005997853,0.002192471,0.276354],"study_design_scores_gemma":[0.005014483,0.00028304,0.006216752,0.003212529,0.0003373479,0.00009882334,0.03292004,0.9096742,0.002036603,0.03785384,0.0006010556,0.001751242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8098392,0.0008917131,0.1849345,0.0004903048,0.0008488749,0.0009893104,0.0006924814,0.0008755153,0.0004380121],"genre_scores_gemma":[0.9946498,0.0003253187,0.001493573,0.00005055371,0.000580059,0.0001350373,0.002554234,0.0001651771,0.00004629774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3137189,"threshold_uncertainty_score":0.9997504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03241177522925837,"score_gpt":0.2850982810516333,"score_spread":0.252686505822375,"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."}}