{"id":"W3048409423","doi":"10.48550/arxiv.2008.05735","title":"Reliability of Decision Support in Cross-spectral Biometric-enabled Systems","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Reliability (semiconductor); Computer science; Decision support system; Situation awareness; Facial expression; Face (sociological concept); Artificial intelligence; Situational ethics; Facial recognition system; Expression (computer science); Machine learning; Risk analysis (engineering); Pattern recognition (psychology); Psychology; Engineering; Social psychology","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":[0.00964225,0.0008602795,0.0009306552,0.001289631,0.000500132,0.001851955,0.0006939294,0.001184068,0.001235982],"category_scores_gemma":[0.07029714,0.0002725332,0.0003814477,0.0007579124,0.0006764715,0.001978555,0.001736455,0.0007148089,0.000429179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007686877,"about_ca_system_score_gemma":0.0006298177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114452,"about_ca_topic_score_gemma":0.0006261035,"domain_scores_codex":[0.9862823,0.005622628,0.00118526,0.00152698,0.004738251,0.0006446005],"domain_scores_gemma":[0.9205071,0.05769001,0.007402381,0.005056099,0.008367307,0.0009770215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01258863,0.001301354,0.1582325,0.0008768953,0.0009922545,0.0008444259,0.001813939,0.3924958,0.05613998,0.004339554,0.00142638,0.3689483],"study_design_scores_gemma":[0.00009887177,0.00374713,0.08261653,0.0001259986,0.0001832744,0.0006126189,0.0006426356,0.8542519,0.05107712,0.005465412,0.0009920391,0.000186545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9220788,0.0007325347,0.07468478,0.0001769349,0.00006947912,0.00009139245,0.0001559873,0.0003841936,0.00162583],"genre_scores_gemma":[0.9945555,0.00005711141,0.005109348,0.000016085,0.00001334514,0.00001822103,0.00005427023,0.00001213042,0.0001639894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00964225,"threshold_uncertainty_score":0.05099368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07052969286621319,"score_gpt":0.2220399377711789,"score_spread":0.1515102449049657,"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."}}