{"id":"W2528508044","doi":"10.1016/j.csndt.2016.09.002","title":"High resolution pore size analysis in metallic powders by X-ray tomography","year":2016,"lang":"en","type":"article","venue":"Case Studies in Nondestructive Testing and Evaluation","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Porosity; Metallography; Materials science; Gas pycnometer; Raw material; Tomography; Volume (thermodynamics); Resolution (logic); Metal powder; Mineralogy; Metallurgy; Composite material; Metal; Computer science; Microstructure; Optics; Artificial intelligence; Chemistry","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":[0.0007294384,0.0001653784,0.0002804667,0.0002511671,0.00008487636,0.00001786964,0.00003436501,0.0000505116,0.00002778215],"category_scores_gemma":[0.001558371,0.0001265361,0.00002850575,0.0006913271,0.000137267,0.0001740453,0.00002759632,0.00006479069,8.63444e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492267,"about_ca_system_score_gemma":0.000009989851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017785,"about_ca_topic_score_gemma":0.0001151851,"domain_scores_codex":[0.9990097,0.0001205947,0.0002621783,0.0002587722,0.0001533561,0.0001953532],"domain_scores_gemma":[0.9987153,0.0009447066,0.00006597947,0.00009607098,0.0001499217,0.00002795929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003938724,0.000258766,0.2684545,0.002303118,0.007441083,0.001925074,0.01629073,0.2194856,0.1364111,0.0007260659,0.001545885,0.3447642],"study_design_scores_gemma":[0.007625949,0.0005675881,0.8055645,0.001512661,0.003315347,0.0006329342,0.01049106,0.03931743,0.0317831,0.09649286,0.00008150007,0.002615135],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976756,0.001438133,0.0002812611,0.00002494552,0.0001522311,0.0001777339,0.00004226813,0.00007507647,0.0001327678],"genre_scores_gemma":[0.9973975,0.000151301,0.002298284,0.000006352121,0.00004178048,0.00007585615,0.0000100896,0.00001288083,0.000005924746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.53711,"threshold_uncertainty_score":0.5159991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04097271468133135,"score_gpt":0.2956015083887942,"score_spread":0.2546287937074629,"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."}}