{"id":"W4407956455","doi":"10.1016/j.jrmge.2025.01.025","title":"Anisotropic mechanical characterization of gneissic rock from Canadian Shield: Bridging the micro- and meso-scale gap","year":2025,"lang":"en","type":"article","venue":"Journal of Rock Mechanics and Geotechnical Engineering","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection; University of Toronto; Energi Simulation","keywords":"Anisotropy; Bridging (networking); Geology; Characterization (materials science); Shield; Scale (ratio); Materials science; Geotechnical engineering; Petrology; Physics; Nanotechnology; Optics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001292914,0.0003676542,0.000218031,0.001675242,0.0008761457,0.000487302,0.0003730404,0.000389889,0.0007744679],"category_scores_gemma":[0.0002779787,0.0002027846,0.0001376323,0.0007263251,0.0008513843,0.000188535,0.0002376203,0.0002864731,0.0001380671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009869311,"about_ca_system_score_gemma":0.001029992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2675216,"about_ca_topic_score_gemma":0.5730887,"domain_scores_codex":[0.9998036,0.000003305195,0.000006171396,0.00003407777,0.0001172947,0.0000355912],"domain_scores_gemma":[0.9997806,0.00002194221,0.00003569957,0.00001087623,0.0001193648,0.0000314286],"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":[0.00006080822,0.00003007258,0.06457273,0.0001055684,0.00002669156,0.0005250254,0.0003404382,0.002470344,0.9242431,0.0001605364,0.0001592877,0.007305404],"study_design_scores_gemma":[0.000005915445,0.00004777336,0.9101275,0.00001443442,0.00002766719,0.000376978,0.0006354882,0.005171719,0.08182315,0.00006631902,0.001675663,0.0000273762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975379,0.0001374052,0.0005510051,0.00003372524,0.00000249419,0.00001310823,0.0003706685,0.00001963614,0.001334174],"genre_scores_gemma":[0.9981358,0.000113714,0.0008650662,0.00002445113,0.000002756677,0.000009073401,0.000298123,0.000008715093,0.0005421772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7324784,"threshold_uncertainty_score":0.5319289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005382208171999669,"score_gpt":0.18607882525991,"score_spread":0.1806966170879103,"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."}}