{"id":"W1928049887","doi":"10.1002/jrs.4684","title":"Study of both fingerprint and high wavenumber Raman spectroscopy of pathological nasopharyngeal tissues","year":2015,"lang":"en","type":"article","venue":"Journal of Raman Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Natural Science Foundation of China","keywords":"Raman spectroscopy; Fingerprint (computing); Nasopharyngeal carcinoma; Linear discriminant analysis; Principal component analysis; Nasopharyngeal cancer; Analytical Chemistry (journal); Chemistry; Receiver operating characteristic; Nuclear magnetic resonance; Spectroscopy; Optics; Medicine; Internal medicine; Chromatography; Physics; Artificial intelligence; Computer science","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":[0.001446112,0.0004408014,0.0002946867,0.0009494071,0.0002357176,0.0004928814,0.0002213602,0.0004975761,0.0003950881],"category_scores_gemma":[0.001499696,0.0002560684,0.000323925,0.0004202338,0.0003562304,0.0006192193,0.0003153028,0.000323575,0.0001677389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319853,"about_ca_system_score_gemma":0.0001782334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005455178,"about_ca_topic_score_gemma":0.0008003943,"domain_scores_codex":[0.9991732,0.0001949016,0.00003873544,0.0002250816,0.0002917048,0.00007638922],"domain_scores_gemma":[0.9993241,0.0002790634,0.0001219355,0.00007125749,0.0001719955,0.00003157801],"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":[0.0002116093,0.0000600265,0.02936148,0.0001750174,0.0001041021,0.00020215,0.0001676484,0.00122896,0.9208407,0.0002512436,0.00007853962,0.0473184],"study_design_scores_gemma":[0.00001230483,0.00053677,0.2134457,0.00003520272,0.0002733616,0.003625574,0.000526475,0.03899806,0.7397787,0.0006687375,0.002040578,0.00005849089],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284203,0.002073244,0.0680097,0.00005744293,0.00002524363,0.00002977671,0.00009744831,0.0001155828,0.001171282],"genre_scores_gemma":[0.9764403,0.0006688002,0.02213377,0.00004207951,0.00001686006,0.00002432629,0.00009943265,0.0000235792,0.0005508123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001446112,"threshold_uncertainty_score":0.007647872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230819849988014,"score_gpt":0.342463509313586,"score_spread":0.3193815243147846,"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."}}