{"id":"W4414654385","doi":"10.1016/j.cemconres.2025.108047","title":"Coupling EDS hypermaps and X-ray microtomography for advanced 3D microstructure characterization of cement paste: A step forward in multiscale modeling","year":2025,"lang":"en","type":"article","venue":"Cement and Concrete Research","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Université de Sherbrooke","keywords":"Multiscale modeling; Microstructure; Segmentation; Image segmentation; Image processing; Characterization (materials science); Residual; Thresholding","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005275991,0.0005876468,0.000417706,0.0009553595,0.0001501031,0.001091583,0.000648864,0.0007446493,0.001135827],"category_scores_gemma":[0.0008995703,0.0004578374,0.0005735641,0.0007591007,0.0003676271,0.0009081781,0.000668351,0.0006049322,0.0002652948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004883694,"about_ca_system_score_gemma":0.0006791768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002703817,"about_ca_topic_score_gemma":0.004406396,"domain_scores_codex":[0.9998438,0.00002630249,0.000008512273,0.00003372698,0.00007731337,0.00001026557],"domain_scores_gemma":[0.999785,0.0001094091,0.00002420543,0.00003120091,0.00004066836,0.000009505462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005641743,0.0001378592,0.003640689,0.0002848839,0.0001166269,0.0001825691,0.0002172185,0.6856275,0.1063762,0.01578742,0.001106263,0.1864663],"study_design_scores_gemma":[0.000002256791,0.000008736434,0.0006772968,0.000006307783,0.000004015088,0.0000222513,0.00001543283,0.9883139,0.006457802,0.003004393,0.001476527,0.00001103128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0247709,0.0002539381,0.9727691,0.0001339485,0.00001802072,0.00004803978,0.0001224402,0.0007983934,0.001085272],"genre_scores_gemma":[0.322429,0.0008498135,0.6741844,0.00008905106,0.00002656986,0.0001592207,0.0002914311,0.0003239626,0.001646541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002703817,"threshold_uncertainty_score":0.00537622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390181768882691,"score_gpt":0.2911511059712487,"score_spread":0.2772492882824218,"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."}}