{"id":"W4200290160","doi":"10.1088/1742-6596/2139/1/012001","title":"Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo","funders":"","keywords":"Regression; Range (aeronautics); Regression analysis; Computer science; Sensitivity (control systems); Artificial intelligence; Pattern recognition (psychology); Machine learning; Statistics; Mathematics; Materials science; Engineering; Electronic engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00008954878,0.0001283921,0.0002255622,0.00002227697,0.0000857228,0.0001140784,0.00005059003,0.00007632717,0.000006713321],"category_scores_gemma":[0.00008472228,0.0001009754,0.00006024442,0.00008733434,0.00002600561,0.000382842,0.00001001741,0.0002640235,7.773192e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002792531,"about_ca_system_score_gemma":0.00003368913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.45031e-7,"about_ca_topic_score_gemma":6.356792e-7,"domain_scores_codex":[0.9993375,0.00003540263,0.0002077386,0.00009920376,0.0001929571,0.0001272047],"domain_scores_gemma":[0.999196,0.00007786256,0.00008836681,0.00008192534,0.0004934899,0.00006235583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001957794,0.00006721161,0.0002979456,0.00022022,0.0001342313,0.00001542194,0.001219145,0.6370348,0.280607,0.01089552,0.0000569943,0.06925566],"study_design_scores_gemma":[0.0009059639,0.0003787801,0.001718081,0.0008702488,0.00007594109,0.00002083124,0.002840149,0.5925848,0.3749481,0.02491334,0.0004411076,0.0003026404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535418,0.0006238609,0.04459634,0.0002606985,0.0003335446,0.0001163001,0.000005494183,0.00005040463,0.0004716322],"genre_scores_gemma":[0.9956331,0.0004012891,0.003700612,0.00001132182,0.0001371995,0.000001379662,0.000006426808,0.00001868687,0.00008999022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09434104,"threshold_uncertainty_score":0.4117658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04270478665234564,"score_gpt":0.2632637300940768,"score_spread":0.2205589434417311,"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."}}