{"id":"W3216677982","doi":"10.1039/d1cp03301h","title":"Electrochemical surface-enhanced Raman spectroscopy (EC-SERS): a tool for the identification of polyphenolic components in natural lake pigments","year":2021,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"Research Nova Scotia; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Camille and Henry Dreyfus Foundation","keywords":"Raman spectroscopy; Polyphenol; Surface-enhanced Raman spectroscopy; Pigment; Substrate (aquarium); Chemistry; Nanotechnology; Quercetin; Materials science; Organic chemistry; Raman scattering; 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":[0.00007687299,0.0002814351,0.0005040545,0.000006131178,0.0001131359,0.000140962,0.0003580996,0.00006099134,0.0001429769],"category_scores_gemma":[0.00008444376,0.0002250278,0.0003370482,0.0001493815,0.000275641,0.0001378886,0.0001141242,0.0002764636,0.0000117598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009988229,"about_ca_system_score_gemma":0.00003846546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001896756,"about_ca_topic_score_gemma":0.00001663643,"domain_scores_codex":[0.9982367,0.00002506449,0.000544345,0.000451956,0.0003436454,0.000398334],"domain_scores_gemma":[0.9988628,0.0002116473,0.0002499715,0.0004024643,0.0002189486,0.00005414549],"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":[0.0001119775,0.000516355,0.00001285542,0.000186997,0.0001127761,6.61445e-7,0.001279298,0.00001676477,0.9963287,0.001273702,0.00007750405,0.00008244345],"study_design_scores_gemma":[0.0006299515,0.000009435439,0.00002951235,0.0000362585,0.0001093436,4.513158e-7,0.0002781101,0.002668145,0.9918559,0.003799709,0.0003205285,0.0002626806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985992,0.00007003248,0.0001580181,0.0002246537,0.00008809342,0.0002180579,0.0001284868,0.00004150976,0.0004719694],"genre_scores_gemma":[0.9976621,0.000007651074,0.0001223319,0.00005412883,0.0009979358,0.00005497556,0.0006569847,0.00003144673,0.0004125054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004472792,"threshold_uncertainty_score":0.9176369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02049975496066115,"score_gpt":0.2447930091257272,"score_spread":0.224293254165066,"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."}}