{"id":"W4297229371","doi":"10.3390/min12101210","title":"Identifying Pseudorutile and Kleberite Using Raman Spectroscopy","year":2022,"lang":"en","type":"article","venue":"Minerals","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Geological Survey of Canada; Natural Resources Canada; Saint Mary's University","funders":"Offshore Energy Research Association","keywords":"Raman spectroscopy; Ilmenite; Rutile; Goethite; Analytical Chemistry (journal); Mineral; Lepidocrocite; Diagenesis; Mineralogy; Materials science; Geology; Chemistry; Optics; Physics; Metallurgy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002576261,0.00008585418,0.0001083054,0.00004462691,0.0003503484,0.0001215432,0.0003472758,0.00001747682,0.0002457007],"category_scores_gemma":[0.00002188087,0.00009138582,0.00002912467,0.0002127251,0.00002450773,0.000156975,0.0006656562,0.0001221689,0.00000407049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002533336,"about_ca_system_score_gemma":0.00001753159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004913602,"about_ca_topic_score_gemma":0.000001388378,"domain_scores_codex":[0.99916,0.00004833488,0.0001236417,0.0002886888,0.0001479402,0.0002313937],"domain_scores_gemma":[0.9995867,0.00002457117,0.00005559371,0.0002655989,0.0000183285,0.0000491889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002148493,0.00002023872,0.0007688839,0.00001869667,0.000007608173,0.00005210533,0.0005498446,0.0007948113,0.9880649,0.005805172,0.003562436,0.0003531808],"study_design_scores_gemma":[0.00125064,0.0001765682,0.001511655,0.00004945598,0.00002805361,0.001414458,0.0007461541,0.3752068,0.288452,0.06318494,0.2668361,0.001143169],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443106,0.0004390452,0.03533653,0.002681212,0.0003779147,0.00011178,0.000003509853,0.0001412988,0.01659807],"genre_scores_gemma":[0.9641616,0.000003675207,0.03025954,0.0003611765,0.00006033629,0.00001068358,0.000002720793,0.000002227879,0.005137981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6996129,"threshold_uncertainty_score":0.3726605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122307249291028,"score_gpt":0.2647273559575063,"score_spread":0.2335042834645961,"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."}}