{"id":"W3049696202","doi":"10.1016/j.chemgeo.2020.119819","title":"A Raman spectroscopic tool to estimate chemical composition of natural volcanic glasses","year":2020,"lang":"en","type":"article","venue":"Chemical Geology","topic":"Glass properties and applications","field":"Materials Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Università degli Studi di Torino; Università degli Studi di Perugia; Ludwig-Maximilians-Universität München","keywords":"Raman spectroscopy; Silicate; Mineralogy; Analytical Chemistry (journal); Silicate glass; Chemical composition; Spectral line; Volcano; Geology; Materials science; Chemistry; Geochemistry; Optics; Physics; Environmental chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003671071,0.0001094775,0.0002393987,0.00001337145,0.0000298643,0.00001482376,0.000276046,0.00007536572,0.0004250765],"category_scores_gemma":[0.00009382123,0.00009416787,0.00004598619,0.0001258167,0.0001528299,0.00004151248,0.000191372,0.0001159451,0.0003409208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002749705,"about_ca_system_score_gemma":0.00002646643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000159198,"about_ca_topic_score_gemma":0.000001021056,"domain_scores_codex":[0.9990826,0.00001462434,0.0002457721,0.0002981588,0.0001035716,0.0002552837],"domain_scores_gemma":[0.9995659,0.00004092826,0.00005871699,0.0001671337,0.0000573143,0.0001100233],"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":[0.00005845366,0.00003605114,0.0001635522,0.00003067738,0.000003475421,0.000001310436,0.00007782875,0.000009961793,0.9965385,0.001893816,0.001072047,0.0001143633],"study_design_scores_gemma":[0.000235887,0.00005455558,0.0002686299,0.000009134686,0.0000110733,0.00000792379,0.000009870754,0.0007998204,0.9970807,0.0003434417,0.001069409,0.0001095253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926215,0.00005234824,0.0006232621,0.006099298,0.00008095654,0.0001666963,0.000005957112,0.00007551835,0.000274405],"genre_scores_gemma":[0.9919233,0.000001254784,0.00683675,0.001025407,0.0001191695,0.00003697526,0.00002246313,0.000008946909,0.00002577714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006213488,"threshold_uncertainty_score":0.465429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256474033008639,"score_gpt":0.2665273960441252,"score_spread":0.2539626557140388,"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."}}