{"id":"W7116421404","doi":"10.5281/zenodo.17989667","title":"Face Recognition - Based Attendance Management System","year":2003,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Attendance; Convolutional neural network; Facial recognition system; Feature (linguistics); Histogram; Local binary patterns; Management system; Face (sociological concept)","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":[0.0005530466,0.0004843881,0.0008906904,0.001306892,0.0006607689,0.0007344507,0.00147853,0.0008259923,0.01058357],"category_scores_gemma":[0.001312801,0.0002065211,0.0003663141,0.000479541,0.0001673281,0.0007816454,0.0009486462,0.0006043841,0.007788112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008080535,"about_ca_system_score_gemma":0.0007079961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003523697,"about_ca_topic_score_gemma":0.003021274,"domain_scores_codex":[0.999298,0.00006402346,0.00003826466,0.0002132504,0.0002934535,0.00009309151],"domain_scores_gemma":[0.9993315,0.00005991057,0.00007707033,0.0001191366,0.000344847,0.00006754942],"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":[0.000775216,0.0005277898,0.008914175,0.0001928467,0.00008980955,0.0003766881,0.000166772,0.007320046,0.1556803,0.002383512,0.04137602,0.7821968],"study_design_scores_gemma":[0.0001463809,0.0007011155,0.04729216,0.00008955839,0.000230083,0.002110427,0.0002114211,0.6020414,0.2825864,0.00346605,0.06081051,0.0003144917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1711888,0.0006328942,0.7158194,0.0009558394,0.0006571167,0.000949056,0.004215773,0.06796704,0.03761409],"genre_scores_gemma":[0.8223694,0.0002876942,0.1346841,0.0005435513,0.0002309548,0.0005679537,0.002919658,0.0003048222,0.03809181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058357,"threshold_uncertainty_score":0.03540558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0353522610387379,"score_gpt":0.2240967139061755,"score_spread":0.1887444528674376,"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."}}