{"id":"W2391889425","doi":"","title":"Situation facing PetroChina′s oil and gas exploration and direction of onshore oil and gas resources strategic area selection","year":2006,"lang":"en","type":"article","venue":"Dizhi tongbao","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"Foreland basin; Fossil fuel; Geology; Resource (disambiguation); Petroleum; Petroleum engineering; Work (physics); China; Selection (genetic algorithm); Unconventional oil; Structural basin; Geomorphology; Paleontology; Geography; Engineering; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001619802,0.000152973,0.0001937937,0.0002304563,0.0001406968,0.0001049229,0.0000276749,0.00009287595,0.00001452307],"category_scores_gemma":[0.00001752566,0.0001486419,0.00003071147,0.0002951545,0.00004325695,0.0004284802,0.00001356281,0.000109494,0.000001912552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003642804,"about_ca_system_score_gemma":0.000007523865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003008298,"about_ca_topic_score_gemma":0.001470495,"domain_scores_codex":[0.9991693,0.00004853807,0.0002550894,0.0002151317,0.0001694338,0.0001424717],"domain_scores_gemma":[0.9996997,0.00003242942,0.00006891428,0.0000873578,0.00005448083,0.00005712762],"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":[0.0001261775,0.0001607805,0.0115269,0.001031878,0.0002763362,0.000008361134,0.010369,0.3856523,0.352245,0.001509743,0.0004107259,0.2366828],"study_design_scores_gemma":[0.0007295954,0.00009686668,0.003341214,0.0001181061,0.0001536133,0.00002189299,0.002872214,0.9707446,0.01653257,0.00387969,0.001092595,0.0004170923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988641,0.0005845866,0.001647274,0.0003427569,0.00004786381,0.00002206888,0.000003907243,0.000137713,0.008572851],"genre_scores_gemma":[0.9961115,0.002692044,0.0001556181,0.000004982744,0.00007615692,0.00001573768,0.00005912351,0.00001859167,0.0008662081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5850923,"threshold_uncertainty_score":0.6061442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488139691183014,"score_gpt":0.2102415138361461,"score_spread":0.195360116924316,"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."}}