{"id":"W4286811363","doi":"10.2139/ssrn.4170645","title":"Self-Remediation of Trace and Heavy Metal Content in Flowback Waters from Hydraulic Fracturing","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hydraulic fracturing; Environmental remediation; TRACE (psycholinguistics); Petroleum engineering; Environmental science; Trace metal; Geology; Mining engineering; Waste management; Metal; Engineering; Metallurgy; Contamination; Materials science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001404956,0.0002040574,0.0002519995,0.0003832478,0.0003631346,0.0004305228,0.0002729019,0.0003962279,0.0009281407],"category_scores_gemma":[0.0002838779,0.0001006055,0.0002234187,0.0001938156,0.0002498402,0.0004050945,0.0003257456,0.0002882924,0.0001838272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003294403,"about_ca_system_score_gemma":0.0002954547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004987501,"about_ca_topic_score_gemma":0.005830152,"domain_scores_codex":[0.9998926,0.00001028752,0.000007811085,0.00002397944,0.00004152702,0.00002374871],"domain_scores_gemma":[0.9999118,0.00001676482,0.00001852789,0.000006910202,0.00003654395,0.000009409945],"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":[0.0006914078,0.00008469629,0.01992536,0.0001002026,0.00002210227,0.0002263732,0.0003400077,0.001399765,0.9630558,0.0001268882,0.0001455071,0.01388194],"study_design_scores_gemma":[0.00001669264,0.0004964534,0.04236947,0.00001685625,0.00004023674,0.0001186602,0.0005355643,0.007977248,0.9470586,0.0001860074,0.00116808,0.00001627351],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991155,0.00004227749,0.0004259671,0.00001271534,0.000005276124,0.000004981176,0.00005858654,0.00001317527,0.000321546],"genre_scores_gemma":[0.9978225,0.00005502537,0.0004408632,0.00001233401,0.000002873113,0.000005102089,0.0001021833,0.000005505006,0.001553672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004987501,"threshold_uncertainty_score":0.009916961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006512196992914047,"score_gpt":0.1904307283477398,"score_spread":0.1839185313548257,"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."}}