{"id":"W2108921602","doi":"10.1366/12-06839","title":"A Fast, Automated, Polynomial-Based Cosmic Ray Spike–Removal Method for the High-Throughput Processing of Raman Spectra","year":2013,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Spectral line; Noise (video); Filter (signal processing); Computer science; Data processing; Algorithm; Polynomial; Raman spectroscopy; Optics; Physics; Computational physics; Mathematics; Artificial intelligence; Mathematical analysis; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004669882,0.0003317221,0.0003931522,0.00007382846,0.000196825,0.00007692506,0.000759698,0.0003001546,0.0001760183],"category_scores_gemma":[0.00009021755,0.0002370865,0.0001926183,0.000301282,0.0004880453,0.00000661624,0.0001638335,0.0002988699,0.00001844198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008146268,"about_ca_system_score_gemma":0.0002556907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001278349,"about_ca_topic_score_gemma":0.000007791483,"domain_scores_codex":[0.9976587,0.00004406749,0.0004584134,0.000660724,0.000426169,0.0007519547],"domain_scores_gemma":[0.9986928,0.0001507555,0.0001953885,0.0006668517,0.0001278758,0.0001663798],"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":[0.0003178233,0.0001450646,0.00001999135,0.00008488108,0.00005658631,0.000001053247,0.00001453223,0.00001833487,0.9681098,0.0007926793,0.02671282,0.003726372],"study_design_scores_gemma":[0.001137856,0.0004563961,0.0001254628,0.00002278119,0.00004295379,0.000007367799,0.00004267455,0.005060582,0.9755787,0.001974816,0.0152768,0.0002735741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1202883,0.000724157,0.8691732,0.002046948,0.0001301822,0.002325112,0.00004833489,0.0003076367,0.004956068],"genre_scores_gemma":[0.6308774,0.00006764737,0.3666398,0.000726218,0.0004852338,0.0007319233,0.0001196547,0.00006774851,0.0002843712],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5105891,"threshold_uncertainty_score":0.9668106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008591609976775792,"score_gpt":0.3205392588268604,"score_spread":0.3119476488500846,"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."}}