{"id":"W4220728146","doi":"10.1111/jmi.13099","title":"Simple automation of SEM‐EDS spectral maps analysis with Python and the edxia framework","year":2022,"lang":"en","type":"article","venue":"Journal of Microscopy","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Python (programming language); Workflow; Computer science; Automation; Simple (philosophy); Cluster analysis; Spectral clustering; Software engineering; User Friendly; Computational science; Programming language; Database; Artificial intelligence; Engineering; Mechanical engineering","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":[0.0007660742,0.00007416894,0.0003007619,0.0001160952,0.0001612412,0.00003120782,0.0001647649,0.00002377106,0.0008125619],"category_scores_gemma":[0.00006332943,0.00003942534,0.000126456,0.0009039947,0.00009996679,0.00007322826,0.00001267464,0.0003306448,0.000002477222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004114448,"about_ca_system_score_gemma":0.00003284684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00024846,"about_ca_topic_score_gemma":0.0000399075,"domain_scores_codex":[0.9989003,0.0002976652,0.0002683311,0.00008529833,0.0003051121,0.0001432998],"domain_scores_gemma":[0.9989935,0.0004833994,0.0003221931,0.00009132573,0.00004651451,0.00006307701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006110696,0.0002977469,0.6760998,0.00008638995,0.002065432,0.00007089227,0.003064529,0.122901,0.005589196,0.001024887,0.002776391,0.179913],"study_design_scores_gemma":[0.001149076,0.001752271,0.9358512,0.00001426483,0.0007065926,0.00009175129,0.0003336695,0.01440629,0.003878523,0.038951,0.002709111,0.0001562371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912203,0.0009433163,0.006804901,0.0006991662,0.00008680582,0.0000605837,0.00002629436,0.000004010264,0.0001546385],"genre_scores_gemma":[0.9832086,0.00004854093,0.01643472,0.000166908,0.00008050722,2.797317e-7,0.000006004274,0.000001654592,0.00005276014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2597513,"threshold_uncertainty_score":0.8896983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005570219158931283,"score_gpt":0.2424835303828086,"score_spread":0.2369133112238774,"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."}}