{"id":"W4321458183","doi":"10.1117/1.jbo.28.2.025002","title":"Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids","year":2023,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Polytechnique Montréal","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Workflow; Data processing; Raman spectroscopy; Functional near-infrared spectroscopy; Signal processing; Software; Artificial intelligence; Algorithm; Computer hardware; Digital signal processing; Database; Optics","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":[0.002496622,0.0002276011,0.0004306525,0.000232904,0.0001362238,0.00018735,0.001739176,0.0002954994,0.00008376725],"category_scores_gemma":[0.001299822,0.000187099,0.000045103,0.0006565675,0.000424759,0.00003899303,0.003158016,0.0003730951,0.000003193681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004050078,"about_ca_system_score_gemma":0.000186956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007499504,"about_ca_topic_score_gemma":0.00001308236,"domain_scores_codex":[0.9970546,0.0001421867,0.000917599,0.0005508665,0.0007137613,0.0006209759],"domain_scores_gemma":[0.9984215,0.0001175188,0.0002864548,0.0006235394,0.0001129751,0.0004380342],"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":[0.0001090279,0.0002087218,0.0002105794,0.00003346812,0.00005141972,0.0003592104,0.00002206467,7.160596e-8,0.9505046,0.000003596799,0.02132578,0.02717147],"study_design_scores_gemma":[0.002725081,0.0007754551,0.0002104061,0.0002319979,0.0000452891,0.0001173277,0.0001598746,0.009320077,0.8725289,0.000475743,0.1131189,0.000290981],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9070711,0.001192821,0.0855517,0.002505187,0.0001993081,0.0005232149,0.002765784,0.00005755543,0.0001333678],"genre_scores_gemma":[0.4268079,0.003383258,0.5296013,0.000758653,0.002712545,0.00002081322,0.03630019,0.0001478684,0.0002674721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4802632,"threshold_uncertainty_score":0.7629676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03866857796744486,"score_gpt":0.398867033268812,"score_spread":0.3601984553013672,"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."}}