{"id":"W3098843416","doi":"10.2118/201287-ms","title":"Acoustic Data Driven Application of Principal Component Multivariate Regression Analysis in the Development of Unconfined Compressive Strength Prediction Models for Shale Gas Reservoirs","year":2020,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Compressive strength; Principal component analysis; Multivariate statistics; Oil shale; Geology; Predictive modelling; Linear regression; Statistics; Petroleum engineering; Mathematics; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001555854,0.0009430686,0.0005593802,0.0008721785,0.0002549585,0.0009048065,0.0007933204,0.0005527752,0.001161066],"category_scores_gemma":[0.003003192,0.0003668002,0.000967049,0.0006186509,0.0002155191,0.000537842,0.0006082179,0.00114513,0.0004630147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005588623,"about_ca_system_score_gemma":0.001284336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0221537,"about_ca_topic_score_gemma":0.01475612,"domain_scores_codex":[0.9995776,0.0001345332,0.00002906798,0.0001061222,0.0001122898,0.00004043784],"domain_scores_gemma":[0.9986941,0.0006709206,0.0001037039,0.0000687971,0.000424337,0.00003821106],"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":[0.00007323826,0.0001254727,0.008264176,0.00007374203,0.00008842202,0.0000712375,0.0000407354,0.9434342,0.003562439,0.0004994415,0.000580002,0.04318686],"study_design_scores_gemma":[0.000001484882,0.00001039747,0.0007333337,0.000002251925,0.000004207227,0.000002482406,0.000005198463,0.9985327,0.0005469553,0.00007190547,0.00008540729,0.000003669889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5379748,0.0004315214,0.453579,0.000398814,0.00007254652,0.0002066647,0.001380808,0.003058597,0.002897312],"genre_scores_gemma":[0.9470941,0.0001304675,0.05051497,0.0000313615,0.00001795648,0.0001294954,0.0009814003,0.00008262481,0.001017637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0221537,"threshold_uncertainty_score":0.0440495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05162490887575112,"score_gpt":0.2759242552052332,"score_spread":0.2242993463294821,"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."}}