{"id":"W1998592782","doi":"10.2523/iptc-16722-ms","title":"The Liwan Gas Project: A Case Study of South China Sea Deepwater Drilling Campaign","year":2013,"lang":"en","type":"article","venue":"International Petroleum Technology Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Husky Energy (Canada)","funders":"China National Offshore Oil Corporation; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Casing; Drilling; Petroleum engineering; Geology; Deep water; Mining engineering; Engineering; Oceanography; Mechanical engineering","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":[0.0002019808,0.0001661995,0.0001780884,0.0003355957,0.00009206769,0.00008149161,0.0004962772,0.000118738,0.0000490098],"category_scores_gemma":[0.0001631129,0.0001248717,0.00004158074,0.0002035233,0.00007929226,0.0001176081,0.00009221355,0.0003443107,0.00001917084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004532603,"about_ca_system_score_gemma":0.00002224421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002628076,"about_ca_topic_score_gemma":0.00006912323,"domain_scores_codex":[0.9989824,0.00003721,0.0003462656,0.0001877953,0.0002149765,0.0002313714],"domain_scores_gemma":[0.9991747,0.00009291674,0.00006611834,0.0003533487,0.0002808626,0.00003206724],"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":[0.00001323094,0.0001035223,0.0346868,0.00002777242,0.0003310849,0.0001571275,0.003413736,0.9493466,0.001832184,0.001322798,0.0001784626,0.0085867],"study_design_scores_gemma":[0.00059389,0.0001098664,0.001607459,0.00002343822,0.00001271828,0.0001732277,0.004945011,0.989631,0.001112871,0.001007684,0.0006051966,0.0001776587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9614415,0.00004625226,0.03641914,0.0002030114,0.000460152,0.0002430246,0.000005473888,0.0003982264,0.0007832151],"genre_scores_gemma":[0.9972142,0.00001383203,0.002332175,0.000002999341,0.0000523044,0.0001465404,0.000002855178,0.00002354658,0.0002115691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0402844,"threshold_uncertainty_score":0.5092121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732530174908451,"score_gpt":0.2665042279011726,"score_spread":0.2491789261520881,"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."}}