{"id":"W4308190833","doi":"10.48175/ijarsct-7344","title":"Smart Attendance System using QR Code","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Education and Learning Interventions","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Attendance; Code (set theory); Computer science; Multimedia; Resource (disambiguation); World Wide Web; 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":[0.0007811636,0.0008130717,0.0006691123,0.001493188,0.0004804735,0.0009700975,0.001072696,0.0009466292,0.04698149],"category_scores_gemma":[0.004164286,0.0002485107,0.00042379,0.0007035207,0.0003097612,0.0009425446,0.001047406,0.0006042087,0.02107287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002697066,"about_ca_system_score_gemma":0.0004881872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009091166,"about_ca_topic_score_gemma":0.0009413478,"domain_scores_codex":[0.9987857,0.0002260107,0.000121176,0.0003185162,0.0004242776,0.0001244304],"domain_scores_gemma":[0.996935,0.000678097,0.0004726712,0.0004368926,0.001190067,0.0002872365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002217376,0.0004943766,0.008446966,0.001755768,0.00007033437,0.0009792803,0.001261478,0.001039926,0.09705903,0.006902439,0.09979396,0.7799791],"study_design_scores_gemma":[0.0007862418,0.004870442,0.0369391,0.0009230531,0.0004002673,0.00669313,0.001161281,0.05002149,0.1930172,0.005795142,0.6985832,0.0008094274],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103661,0.002670916,0.6748506,0.002324997,0.002840949,0.004082175,0.01074706,0.1167886,0.07532869],"genre_scores_gemma":[0.5290598,0.001331284,0.3073496,0.001997863,0.0007477655,0.002298479,0.005346815,0.002101664,0.1497668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04698149,"threshold_uncertainty_score":0.1571687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1075709813771785,"score_gpt":0.4684488519773675,"score_spread":0.3608778706001889,"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."}}