{"id":"W1580736391","doi":"10.5772/14934","title":"Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Laser-induced breakdown spectroscopy; Spectroscopy; Mars Exploration Program; Exploration of Mars; Artificial neural network; Engineering; Remote sensing; Process engineering; Computer science; Materials science; Environmental science; Artificial intelligence; Geology; Physics; Astrobiology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001759435,0.0006597226,0.0006144379,0.0001971031,0.0001191623,0.0001459093,0.0002963524,0.0009742101,0.0004677036],"category_scores_gemma":[0.00002310864,0.0006879635,0.0002140525,0.00002033484,0.0001343172,0.00004048386,0.00004181743,0.0007606655,0.00003636686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317314,"about_ca_system_score_gemma":0.00005111701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000182385,"about_ca_topic_score_gemma":0.00006863285,"domain_scores_codex":[0.9979102,0.00001595332,0.0006776667,0.0006499173,0.0002013744,0.0005449037],"domain_scores_gemma":[0.9988172,0.000145123,0.0001475029,0.0005970852,0.00009249806,0.0002005654],"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":[0.0008857003,0.00004439808,0.000003810312,0.0002858969,0.0002908979,0.00007925061,0.000052296,0.0002730059,0.9835482,0.006576517,0.002565876,0.005394104],"study_design_scores_gemma":[0.0002882162,0.0002549483,0.000004147634,0.0001150136,0.0002042719,0.00002714338,0.000002248025,0.09117658,0.8964007,0.007737404,0.003104073,0.0006852573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2810019,0.00005590248,0.03357337,0.0002889137,0.008949629,0.00340029,0.00232532,0.002285965,0.6681187],"genre_scores_gemma":[0.9799308,0.000004883904,0.0002553962,0.0001209259,0.001451557,0.00010251,0.0004889925,0.0001898881,0.01745506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6989289,"threshold_uncertainty_score":0.9995571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0202059009225629,"score_gpt":0.2348498637549601,"score_spread":0.2146439628323972,"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."}}