{"id":"W4400333230","doi":"10.1002/jbio.202400087","title":"Toward noncontact macroscopic imaging of multiple cancers using multi‐spectral inelastic scattering detection","year":2024,"lang":"en","type":"letter","venue":"Journal of Biophotonics","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":"Princess Margaret Cancer Centre; University of Toronto; University Health Network; Montreal Neurological Institute and Hospital; Polytechnique Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Raman scattering; Raman spectroscopy; Spectral imaging; Optics; Nuclear magnetic resonance; Materials science; Inelastic scattering; Resolution (logic); Scattering; Physics; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005002655,0.0003188462,0.0002225884,0.0002655544,0.0001857278,0.0006242704,0.0005440494,0.0012828,0.001696183],"category_scores_gemma":[0.0005228877,0.0003423032,0.0001968857,0.00008433079,0.0007310748,0.0005565364,0.000439805,0.001258669,0.001150732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002649758,"about_ca_system_score_gemma":0.0001903688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001769812,"about_ca_topic_score_gemma":0.000543437,"domain_scores_codex":[0.9997191,0.00005239988,0.000009215301,0.00004816313,0.0001473674,0.0000237561],"domain_scores_gemma":[0.9996707,0.0001674411,0.00003127411,0.00004468825,0.00005446914,0.00003148994],"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":[0.00008449734,0.00005658327,0.0003472279,0.0001691575,0.00001107641,0.0006839055,0.0001027402,0.0005898944,0.9508904,0.004594819,0.004521239,0.03794852],"study_design_scores_gemma":[0.00005695561,0.0002940536,0.001253976,0.00002375284,0.0000194631,0.004523416,0.0001013871,0.03448486,0.9005678,0.003446319,0.05518765,0.00004036862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2177946,0.006181892,0.7284054,0.02148502,0.002358014,0.0002988855,0.0001240755,0.001622703,0.02172952],"genre_scores_gemma":[0.5947465,0.003900546,0.3745428,0.004549399,0.000765726,0.0002163403,0.0001209438,0.0001351924,0.02102257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001696183,"threshold_uncertainty_score":0.005674243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270425167711621,"score_gpt":0.3249878063721705,"score_spread":0.3022835546950542,"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."}}