{"id":"W2780098221","doi":"10.3390/s17122939","title":"Using the Kalman Algorithm to Correct Data Errors of a 24-Bit Visible Spectrometer","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kalman filter; Computer science; Algorithm; MATLAB; Light intensity; Spec#; Spectrometer; Noise (video); Optics; Artificial intelligence; Physics","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":[0.0004482553,0.00009901745,0.0001559819,0.00003143191,0.0003399386,0.0000710021,0.0009591713,0.00002811782,0.0001866993],"category_scores_gemma":[0.0000815313,0.00006784873,0.00005724197,0.0001150137,0.0001618756,0.0001381173,0.0006755592,0.00008604853,0.0001657761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004483525,"about_ca_system_score_gemma":0.000004386938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006611225,"about_ca_topic_score_gemma":0.00006845671,"domain_scores_codex":[0.9989544,0.00006615811,0.0001617009,0.0003024577,0.0002928603,0.0002224258],"domain_scores_gemma":[0.9981636,0.00003050288,0.0001170601,0.001611153,0.00000589921,0.0000717349],"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":[0.0000903714,0.0005479158,0.2417639,0.00006052281,0.000672218,0.0001380093,0.01390252,0.03024054,0.5536317,0.00004297271,0.02082898,0.1380804],"study_design_scores_gemma":[0.001011764,0.0003743182,0.1531426,0.000210811,0.0006721677,0.00006119094,0.004498774,0.2810794,0.5083621,0.0009056994,0.04807004,0.001611181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975834,0.000005002876,0.0005970265,0.0005735455,0.00028594,0.00007494377,0.00002480648,0.00002010938,0.0008352084],"genre_scores_gemma":[0.9848713,0.000003158578,0.01304487,0.00003348141,0.0001268333,7.7598e-7,0.000002971459,0.00001445035,0.001902169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2508388,"threshold_uncertainty_score":0.9994239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1249392120766981,"score_gpt":0.34919414761021,"score_spread":0.2242549355335119,"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."}}