{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00693354,0.00007874961,0.0001514487,0.00008418841,0.0002482189,0.00003613495,0.0003578227,0.00001834955,0.00003677411],"category_scores_gemma":[0.004408419,0.00003124792,0.00005228519,0.0008116385,0.001172841,0.0001530556,0.0001297379,0.0001384108,4.156323e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001952662,"about_ca_system_score_gemma":0.003994541,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008546908,"about_ca_topic_score_gemma":0.007709037,"domain_scores_codex":[0.9966962,0.0001275682,0.0002480441,0.0001946659,0.002409084,0.000324425],"domain_scores_gemma":[0.9960367,0.001943699,0.00007904368,0.0002347508,0.001660702,0.00004510177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001532419,0.0007442872,0.01722153,0.0004586639,0.00005253826,6.731767e-7,0.004535126,0.001187972,0.9147096,0.007590408,0.001603797,0.05036299],"study_design_scores_gemma":[0.002156728,0.001094754,0.06195087,0.0003464778,0.00001532167,4.744616e-7,0.0009513758,0.04785905,0.8840039,0.0008087644,0.0006780172,0.0001342648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968109,0.0000214688,0.00008539084,0.001571279,0.00006809331,0.0008459894,0.0003341483,0.000001433677,0.0002613106],"genre_scores_gemma":[0.9990289,0.000002899555,0.0002686009,0.000004788928,0.00003886356,0.0000891058,0.00000241985,0.000005200312,0.0005592307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05022873,"threshold_uncertainty_score":0.9980553,"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."}}