{"id":"W4410269625","doi":"10.55041/ijsrem47560","title":"Face Recognition Attendance System","year":2025,"lang":"en","type":"article","venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Facial recognition system; Attendance; Computer science; Artificial intelligence; Psychology; Pattern recognition (psychology); Political 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":[0.0005379972,0.000815931,0.0008528762,0.001247545,0.0007523603,0.001054501,0.00167933,0.001268559,0.03176272],"category_scores_gemma":[0.001066993,0.0002329472,0.0005525469,0.0005741037,0.000194305,0.001009628,0.001340353,0.0009496106,0.02375362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00085309,"about_ca_system_score_gemma":0.0008722732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003215532,"about_ca_topic_score_gemma":0.001952961,"domain_scores_codex":[0.9991212,0.00005776946,0.00005312165,0.0002485839,0.000375441,0.000143918],"domain_scores_gemma":[0.999479,0.00004306098,0.00004898789,0.0000943081,0.0002816737,0.00005306896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001337588,0.0005443738,0.005796249,0.0005817298,0.0001255288,0.0007713102,0.0002111023,0.005330955,0.1237412,0.004407761,0.1095216,0.7476306],"study_design_scores_gemma":[0.0003115854,0.00132045,0.02617267,0.0002762675,0.0004041169,0.005454327,0.000236655,0.3449087,0.3703514,0.004537828,0.2455501,0.0004759198],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1433107,0.002768217,0.5942286,0.001846423,0.001773676,0.001623427,0.01099318,0.1395087,0.1039469],"genre_scores_gemma":[0.7553588,0.0009305063,0.1257527,0.001519903,0.0004809011,0.0008927325,0.01235927,0.0006861958,0.1020189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03176272,"threshold_uncertainty_score":0.1062569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0412752673948475,"score_gpt":0.3193533604151194,"score_spread":0.2780780930202719,"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."}}