{"id":"W2000908209","doi":"10.1366/000370207780220813","title":"Use of Chemometrics and Laser-Induced Breakdown Spectroscopy for Quantitative Analysis of Major and Minor Elements in Aluminum Alloys","year":2007,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Chemometrics; Laser-induced breakdown spectroscopy; Multivariate statistics; Univariate; Calibration; Spectroscopy; Matrix (chemical analysis); Analytical Chemistry (journal); Materials science; Laser; Chemistry; Biological system; Mathematics; Optics; Statistics; Chromatography; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006765238,0.0004095496,0.001008143,0.001557574,0.00005963061,0.00004405072,0.0001843687,0.0002286528,0.00004431123],"category_scores_gemma":[0.00008961766,0.0004335868,0.0001333665,0.0024431,0.0001162313,0.0001866054,0.0000555587,0.0002703495,0.000001974911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140975,"about_ca_system_score_gemma":0.00003357038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001755452,"about_ca_topic_score_gemma":0.00106994,"domain_scores_codex":[0.9975234,0.00002079671,0.0009155151,0.0005174241,0.0003218816,0.0007009486],"domain_scores_gemma":[0.9983487,0.0007841201,0.000247884,0.000374394,0.00007138498,0.0001735614],"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":[0.000439781,0.0001475909,0.01244301,0.0001851333,0.000791254,0.000003887546,0.0005072203,0.0001835261,0.9809778,0.00385308,0.0001039006,0.0003637606],"study_design_scores_gemma":[0.00145342,0.0003696776,0.02623634,0.00002564296,0.0005690352,0.000001693669,0.000285944,0.007541021,0.9625825,0.0004253084,0.0001282366,0.0003811501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899624,0.00009810716,0.007888099,0.00002401917,0.000113321,0.0008044871,0.0002156866,0.00006675482,0.0008271022],"genre_scores_gemma":[0.9313659,0.0001635583,0.06819216,0.00003214184,0.00003878125,0.00004233204,0.00008567456,0.00005616533,0.00002330502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06030406,"threshold_uncertainty_score":0.9998116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02495376861313901,"score_gpt":0.27442456708494,"score_spread":0.249470798471801,"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."}}