{"id":"W2537499163","doi":"10.1177/0003702816671065","title":"Automatic Spike Removal Algorithm for Raman Spectra","year":2016,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Princeton University","keywords":"Raman spectroscopy; Spike (software development); Algorithm; Spectral line; Computer science; Analytical Chemistry (journal); Chemistry; Materials science; Physics; Optics; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233071,0.001111993,0.001104061,0.00191525,0.0008791802,0.001234509,0.001895551,0.001653742,0.002686043],"category_scores_gemma":[0.003374399,0.0005152915,0.001427412,0.001812501,0.0005665551,0.001220223,0.001383562,0.002163125,0.002529991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006367298,"about_ca_system_score_gemma":0.001546991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570311,"about_ca_topic_score_gemma":0.004090272,"domain_scores_codex":[0.9988844,0.0001112577,0.00008141327,0.0002307124,0.0005886045,0.0001035768],"domain_scores_gemma":[0.998238,0.0005015075,0.0001208253,0.0002643648,0.0008184413,0.00005687863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002767892,0.0001428912,0.0008299167,0.0001433011,0.00008805344,0.0001392022,0.0001306534,0.04185482,0.1061549,0.00655576,0.006136531,0.8375471],"study_design_scores_gemma":[0.00001884275,0.00005229581,0.0009127068,0.000009183203,0.00001794424,0.000178039,0.00003826576,0.943812,0.04460015,0.005270803,0.005057761,0.00003214577],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004822982,0.00005217499,0.993452,0.00004882278,0.00003097977,0.00003404474,0.00005593023,0.001231717,0.00027134],"genre_scores_gemma":[0.02901049,0.0000720945,0.9688646,0.00005107383,0.00002382261,0.00007389742,0.0004787423,0.0002664648,0.001158807],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002686043,"threshold_uncertainty_score":0.008985758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00862531569395676,"score_gpt":0.301438807915448,"score_spread":0.2928134922214912,"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."}}