{"id":"W3015002515","doi":"10.36227/techrxiv.12061632.v1","title":"Smart Raspberry Pi Bank Safety Deposit box With Facial Recognition: Fintech Case Study","year":2020,"lang":"en","type":"preprint","venue":"","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Raspberry pi; Arduino; Computer security; Lock (firearm); Facial recognition system; Computer science; Embedded system; Operating system; Engineering; Internet of Things; Artificial intelligence; Feature extraction","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"],"consensus_categories":[],"category_scores_codex":[0.0003068894,0.0007978658,0.0009423982,0.0002261236,0.0001702183,0.0001981486,0.0003837214,0.0004830653,0.0005420785],"category_scores_gemma":[0.00003201784,0.0007418911,0.0002176359,0.0003266848,0.00004480244,0.0001167852,0.0003942311,0.001555717,0.0006066617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003820036,"about_ca_system_score_gemma":0.0001357951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003100168,"about_ca_topic_score_gemma":0.006124399,"domain_scores_codex":[0.997016,0.0001635733,0.0008684588,0.0009112505,0.0005219873,0.0005187796],"domain_scores_gemma":[0.9984501,0.0001031493,0.000118814,0.0008845298,0.0001594196,0.0002839887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003515772,0.003951243,0.1071038,0.02571515,0.02231744,0.3415861,0.05406363,0.1487338,0.002316119,0.00008717326,0.1697226,0.1208872],"study_design_scores_gemma":[0.0702448,0.01645805,0.05643928,0.01693164,0.01384909,0.1211269,0.1455311,0.2594647,0.06560517,0.001204367,0.1724877,0.06065727],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8637037,0.0003207353,0.07176352,0.0002328554,0.005125825,0.005523378,0.0004894767,0.004421825,0.04841863],"genre_scores_gemma":[0.9954195,0.000008469454,0.002250463,0.00006635034,0.0008236208,0.0003960944,0.0002416038,0.0002031673,0.0005907149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2204592,"threshold_uncertainty_score":0.9995032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0253391563749274,"score_gpt":0.2271170277743028,"score_spread":0.2017778713993754,"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."}}