{"id":"W2334479576","doi":"10.2118/178960-ms","title":"Optimization of Asset-Wide Chemical Treatment Programs","year":2016,"lang":"en","type":"article","venue":"SPE International Conference and Exhibition on Formation Damage Control","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"ConocoPhillips","keywords":"Computer science; Identification (biology); Risk analysis (engineering); Asset (computer security); Productivity; Resource (disambiguation); Personalization; Scale (ratio); Pipeline transport; Production (economics); Systems engineering; Environmental science; Engineering; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006900567,0.0001057634,0.0001123749,0.0001148476,0.00002187511,0.0000366332,0.00006053454,0.00005352384,0.0002397294],"category_scores_gemma":[0.00002826537,0.00007620842,0.00003792997,0.00004378488,0.00004220091,0.0004892494,0.000006876973,0.00003561084,0.00001881413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007521716,"about_ca_system_score_gemma":0.000008514528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000309439,"about_ca_topic_score_gemma":0.000001928404,"domain_scores_codex":[0.9993986,0.00001475206,0.0002441657,0.0001068319,0.0001478216,0.00008775361],"domain_scores_gemma":[0.9995952,0.00002836112,0.00006967555,0.00009171179,0.0001761476,0.00003885442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002627396,0.0002559153,0.0008758897,0.0000699001,0.0001500684,0.000003017994,0.0003782723,0.004352393,0.08980084,0.0291805,0.001392113,0.8732783],"study_design_scores_gemma":[0.004960067,0.0006969921,0.0007580424,0.0004487073,0.0000435331,0.00002002176,0.0001656826,0.1666679,0.8084604,0.008626917,0.008672413,0.0004793625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2855225,0.00005540317,0.6625449,0.01081846,0.000674339,0.0009112052,0.0002421649,0.00095409,0.03827699],"genre_scores_gemma":[0.9981612,0.0003907463,0.001013871,0.000056013,0.00006510074,0.00005113107,0.0001021671,0.000008312838,0.0001514762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.872799,"threshold_uncertainty_score":0.3107689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01669111837973952,"score_gpt":0.2342189318199618,"score_spread":0.2175278134402223,"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."}}