{"id":"W4388294481","doi":"10.1002/jrs.6611","title":"Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions","year":2023,"lang":"en","type":"article","venue":"Journal of Raman Spectroscopy","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Agencia Estatal de Investigación; Ministerio de Economía y Competitividad; European Commission","keywords":"Raman spectroscopy; Mars Exploration Program; Laser-induced breakdown spectroscopy; Support vector machine; Artificial intelligence; Characterization (materials science); Calcite; Mineral; Machine learning; Analytical Chemistry (journal); Artificial neural network; Materials science; Spectroscopy; Pattern recognition (psychology); Computer science; Biological system; Mineralogy; Chemistry; Nanotechnology; Optics; Physics; Environmental chemistry; Astrobiology","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":[0.004065581,0.0008220784,0.0007649143,0.001881492,0.0003002758,0.001287499,0.0007219043,0.0007983335,0.0008000039],"category_scores_gemma":[0.005596521,0.0002658518,0.0006284575,0.001330196,0.0004417436,0.00127641,0.0007917341,0.0009174073,0.0004115114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004157783,"about_ca_system_score_gemma":0.0005544976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001175582,"about_ca_topic_score_gemma":0.001681815,"domain_scores_codex":[0.9987514,0.0004860882,0.00006450526,0.0002157568,0.0004153344,0.00006696078],"domain_scores_gemma":[0.997413,0.001372644,0.0003307058,0.0002082201,0.0005818004,0.00009373991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006216499,0.0007636998,0.06633529,0.0003844647,0.0006390674,0.00014199,0.0001719923,0.2212995,0.1113104,0.003323975,0.002041156,0.5929669],"study_design_scores_gemma":[0.00001396449,0.0001494347,0.01037528,0.00002728554,0.00004218573,0.0000733091,0.00007171698,0.9613914,0.02398811,0.002950839,0.0008844158,0.0000320792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3925381,0.00267775,0.596078,0.001311039,0.0001460264,0.0001213192,0.0004624326,0.002624994,0.004040272],"genre_scores_gemma":[0.8094806,0.0003846721,0.1887324,0.0001205076,0.00005471559,0.0000600759,0.0001877511,0.00009641114,0.0008827791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004065581,"threshold_uncertainty_score":0.02150112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183891707168372,"score_gpt":0.3140706201011259,"score_spread":0.2922317030294422,"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."}}