{"id":"W4413119948","doi":"10.1016/j.energy.2025.137911","title":"Numerical insights into impact of rock matrix and fracture characteristics on sCO2-enhanced geothermal heat extraction","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection; National Natural Science Foundation of China; Sichuan Province Science and Technology Support Program; Ministry of Natural Resources","keywords":"Extraction (chemistry); Geothermal gradient; Matrix (chemical analysis); Fracture (geology); Geothermal energy; Geology; Geotechnical engineering; Petroleum engineering; Environmental science; Materials science; Chemistry; Composite material; Chromatography; Geophysics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001639544,0.0003084521,0.0003385044,0.0003751295,0.0004243222,0.0006656855,0.0004827255,0.0006910613,0.003666538],"category_scores_gemma":[0.0009697723,0.0001979074,0.0003029925,0.0004633856,0.0005732851,0.0005637486,0.0003283363,0.0002950432,0.0001732804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000523753,"about_ca_system_score_gemma":0.0006442469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01912775,"about_ca_topic_score_gemma":0.01948299,"domain_scores_codex":[0.9999276,0.00000882924,0.000003292972,0.00001119724,0.00002002656,0.00002893881],"domain_scores_gemma":[0.9997285,0.0001565652,0.00002524868,0.00001990951,0.00004666804,0.00002301351],"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":[0.0002363338,0.00008372741,0.00462934,0.00006705037,0.00001948283,0.0003545829,0.00004020757,0.9667289,0.02219498,0.002194045,0.0002822646,0.003169038],"study_design_scores_gemma":[0.00002407437,0.00002190283,0.002293823,0.00000413423,0.000006836975,0.00001857908,0.00004272893,0.9939913,0.003161191,0.0002752256,0.0001516549,0.000008493333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817183,0.0001102271,0.003615313,0.0001878774,0.00002264581,0.00001521788,0.0002399449,0.00007680326,0.01401367],"genre_scores_gemma":[0.9991111,0.00002878784,0.0002674423,0.000007249403,0.00000242496,0.000003440382,0.00003599431,0.000009233699,0.0005342758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01912775,"threshold_uncertainty_score":0.03803283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003956762138772251,"score_gpt":0.241822970927884,"score_spread":0.2378662087891118,"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."}}