{"id":"W2803006909","doi":"10.4043/28743-ms","title":"Efficient Condensate Mercury Removal Offshore","year":2018,"lang":"en","type":"article","venue":"Offshore Technology Conference","topic":"Marine and Offshore Engineering Studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Environmental science; Natural gas; STREAMS; Contamination; Produced water; Waste management; Submarine pipeline; Environmental chemistry; Environmental engineering; Chemistry; Geology; Engineering; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001421273,0.0003961805,0.0004376949,0.0004119832,0.0001449158,0.00003426057,0.0005483923,0.0003493888,0.0003306537],"category_scores_gemma":[0.0001129407,0.0003832336,0.00007613705,0.0006466812,0.0006460384,0.0000369014,0.0002332633,0.0004808324,0.000418384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006871182,"about_ca_system_score_gemma":0.00004071562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005928907,"about_ca_topic_score_gemma":0.00005866251,"domain_scores_codex":[0.9983341,0.00001237322,0.0003516965,0.000425514,0.0002173588,0.0006589845],"domain_scores_gemma":[0.9988286,0.00003629867,0.000040967,0.0006814018,0.0003186952,0.00009407003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001375551,0.000345941,0.01724127,0.0007105842,0.001718094,0.001142159,0.003048096,0.009600308,0.07332999,0.4044518,0.1040719,0.3842023],"study_design_scores_gemma":[0.001694597,0.0007424252,0.01023282,0.0003908192,0.0002418619,0.0007888007,0.001397713,0.4751724,0.09044103,0.007765898,0.4080215,0.003110233],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9047142,0.001235144,0.01120913,0.001408013,0.001592501,0.0003504662,0.00002080998,0.004887879,0.07458191],"genre_scores_gemma":[0.9956498,0.00009913261,0.003173591,0.00007009465,0.0002207517,0.00004245905,0.000008018698,0.00005925241,0.0006769726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4655721,"threshold_uncertainty_score":0.999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01108035039894696,"score_gpt":0.2212860198536059,"score_spread":0.2102056694546589,"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."}}