{"id":"W4392503833","doi":"10.1007/s00603-024-03789-7","title":"Evaluation of Damage Stress Thresholds and Mechanical Properties of Granite: New Insights from Digital Image Correlation and GB-FDEM","year":2024,"lang":"en","type":"article","venue":"Rock Mechanics and Rock Engineering","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Polytechnic University; Research Grants Council, University Grants Committee","keywords":"Digital image correlation; Geology; Stress (linguistics); Seismology; Materials science; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000156717,0.0002332548,0.0002886226,0.0001698943,0.00005495178,0.00009630828,0.00007198755,0.0001549185,0.000004251036],"category_scores_gemma":[0.00005075155,0.0002154368,0.00004319172,0.0001420954,8.827413e-7,0.000408701,0.0001228221,0.000190846,7.05722e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003155541,"about_ca_system_score_gemma":0.000033737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002097384,"about_ca_topic_score_gemma":0.000009060711,"domain_scores_codex":[0.9988273,0.00001175328,0.0003585991,0.000270434,0.0003645734,0.00016738],"domain_scores_gemma":[0.9995546,0.00003839728,0.00003861901,0.0001470827,0.000106683,0.0001146515],"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":[0.00001092996,0.00001502695,0.000003513225,0.000625563,0.0001633888,0.000002866604,0.001115344,0.6397968,0.3418667,0.01131891,0.00001333418,0.00506758],"study_design_scores_gemma":[0.0003484369,0.00005014533,0.00001150907,0.0006781237,0.0001705175,0.000006664616,0.0001385827,0.9523243,0.04202841,0.00397999,0.00004626036,0.000217113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4586321,0.01048601,0.5302899,0.000006538082,0.0002598367,0.0001755046,0.00003268139,0.00009965766,0.0000177815],"genre_scores_gemma":[0.9983586,0.0009276523,0.0005377689,0.000001717438,0.00007761671,0.000008864312,0.00002073678,0.00005746925,0.000009565771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5397265,"threshold_uncertainty_score":0.8785256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589554771436304,"score_gpt":0.2035736889439128,"score_spread":0.1876781412295498,"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."}}