{"id":"W4411210708","doi":"10.26434/chemrxiv-2025-zvz1c","title":"Gaining quantitative fidelity from Raman spectra in regimesof large and varying fluorescence","year":2025,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Raman spectroscopy; Fluorescence; Fidelity; High fidelity; Analytical Chemistry (journal); Chemistry; Computer science; Physics; Optics; Environmental chemistry; Acoustics; Telecommunications","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":[0.00443567,0.0009358061,0.0006711029,0.001108832,0.0005686412,0.001630735,0.00111124,0.0008260089,0.001406918],"category_scores_gemma":[0.01015698,0.0005552275,0.0008898616,0.001125995,0.001056629,0.002360344,0.001428555,0.001574446,0.0004901474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00071088,"about_ca_system_score_gemma":0.0008244562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002236595,"about_ca_topic_score_gemma":0.003570124,"domain_scores_codex":[0.9984119,0.0003271381,0.00009457497,0.0004510119,0.0005851271,0.0001301919],"domain_scores_gemma":[0.9960986,0.002242899,0.0004078107,0.000763473,0.0004306401,0.00005648195],"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":[0.000719177,0.0004478917,0.03202504,0.0006701709,0.000337653,0.0003942876,0.0006936837,0.2650988,0.5473993,0.01516776,0.001129192,0.1359171],"study_design_scores_gemma":[0.00002346217,0.0002373902,0.01521575,0.00006085184,0.00008458422,0.0002702168,0.0002850509,0.6821417,0.2854728,0.0127595,0.003335191,0.0001135113],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5167636,0.0004802196,0.4770207,0.0003448458,0.00005197032,0.00008265745,0.0009377465,0.001454259,0.002863918],"genre_scores_gemma":[0.8203078,0.0004651727,0.1768884,0.0001109514,0.00001544346,0.00007913091,0.00127964,0.0002111237,0.0006422527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00443567,"threshold_uncertainty_score":0.02345836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453773056687326,"score_gpt":0.358255368227425,"score_spread":0.3337176376605517,"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."}}