{"id":"W2526661336","doi":"10.1016/j.ifacol.2016.07.419","title":"Effect of Sensor Noise on Estimation of Diffusion**The financial support of the National Science and Engineering Research Council of Canada Discovery Grant program for the research discussed in this article is gratefully acknowledged.","year":2016,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Research council; Estimation; Noise (video); Engineering research; Science and engineering; Diffusion; Computer science; Economics; Engineering; Engineering ethics; Management; Physics; Telecommunications; Government (linguistics); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003436036,0.0005604682,0.0005813136,0.0005267537,0.0003649627,0.0007155419,0.0004032912,0.0009672629,0.0003811991],"category_scores_gemma":[0.03841149,0.0003805488,0.0003694094,0.0004472798,0.0008912354,0.001298327,0.0006935417,0.000653005,0.0001079513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006527658,"about_ca_system_score_gemma":0.000466367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003672893,"about_ca_topic_score_gemma":0.002868905,"domain_scores_codex":[0.9977614,0.0009624335,0.0001083243,0.0002979458,0.0007619789,0.0001079551],"domain_scores_gemma":[0.9643331,0.03138904,0.001325534,0.001106637,0.001697061,0.0001485369],"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":[0.0009201368,0.00007048377,0.01489711,0.0003455375,0.0001121367,0.0004674518,0.0002807245,0.8564825,0.03773441,0.008651732,0.0006498501,0.0793879],"study_design_scores_gemma":[0.00002099047,0.0001981699,0.01158882,0.00004562587,0.00004314287,0.0003117561,0.00006295986,0.9427423,0.03941977,0.004480768,0.00103367,0.00005189482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2589625,0.001330314,0.7355339,0.0009051557,0.0001696481,0.00003732619,0.0001012425,0.0004005033,0.002559399],"genre_scores_gemma":[0.9579675,0.0003218496,0.04097578,0.00007989055,0.0000216762,0.0000211923,0.00006373081,0.0000703254,0.0004780312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003672893,"threshold_uncertainty_score":0.01817167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04234037216812239,"score_gpt":0.33581446729578,"score_spread":0.2934740951276576,"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."}}