{"id":"W2810200099","doi":"10.1007/s40948-018-0088-4","title":"Fracturing index-based brittleness prediction from geophysical logging data: application to Longmaxi shale","year":2018,"lang":"en","type":"article","venue":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"China University of Petroleum, Beijing; Chinese Academy of Sciences; University of Saskatchewan; National Science Foundation","keywords":"Brittleness; Oil shale; Hydraulic fracturing; Geology; Geotechnical engineering; Fracture (geology); Outcrop; Shale gas; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003480956,0.0004973126,0.0003099299,0.001142039,0.0003217741,0.0004201288,0.0004110906,0.0004412487,0.0008773445],"category_scores_gemma":[0.00106991,0.0002168697,0.0002658496,0.00088022,0.0001568944,0.0003301735,0.0002836104,0.0002518261,0.0002022445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003937905,"about_ca_system_score_gemma":0.0005762005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02652154,"about_ca_topic_score_gemma":0.04924158,"domain_scores_codex":[0.9999522,0.000008228452,0.000004879396,0.00001361396,0.00001378248,0.00000729561],"domain_scores_gemma":[0.9994152,0.0002845773,0.00006563756,0.00003981795,0.0001549716,0.0000398532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001031536,0.0008590922,0.2133671,0.0002043485,0.0001435365,0.0006243005,0.0004287451,0.4884585,0.04021766,0.0003569436,0.001522835,0.2527854],"study_design_scores_gemma":[0.00002052807,0.00006353587,0.04609084,0.000005174454,0.00001426812,0.00002302541,0.00008308582,0.9502574,0.003198592,0.0001229826,0.0001048755,0.00001566451],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955049,0.00005141229,0.003509467,0.00003325199,0.000003496075,0.00001130436,0.0003558911,0.0002058438,0.0003246272],"genre_scores_gemma":[0.995726,0.00004613536,0.003479872,0.000003949011,0.000003358764,0.000008564531,0.0004112743,0.00001320618,0.0003077494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02652154,"threshold_uncertainty_score":0.05273432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006530720780222777,"score_gpt":0.2044044507611323,"score_spread":0.1978737299809095,"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."}}