{"id":"W2549581297","doi":"","title":"Overpressure conditions and reservoir compartmentalization on the Scotian Margin","year":2013,"lang":"en","type":"article","venue":"","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Overpressure; Geology; Margin (machine learning); Hydrostatic pressure; Mechanics","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":[0.0001245707,0.0001002194,0.0001173378,0.0007757528,0.0004674169,0.0006359542,0.0001695533,0.0001713312,0.001121012],"category_scores_gemma":[0.0006167805,0.0001324187,0.0001119691,0.0007018694,0.0006408842,0.0002754889,0.0006020252,0.0001372021,0.00006658591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481703,"about_ca_system_score_gemma":0.001044889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2439484,"about_ca_topic_score_gemma":0.3345335,"domain_scores_codex":[0.9998682,0.000009610965,0.000009356417,0.00002803296,0.00004257458,0.00004232093],"domain_scores_gemma":[0.9996268,0.00004639412,0.0001423643,0.00002327903,0.00009776676,0.00006343018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002518381,0.00001604019,0.96502,0.00003498178,0.0000313412,0.001115432,0.000815746,0.002882219,0.02341952,0.0004121212,0.0001290707,0.00587165],"study_design_scores_gemma":[0.000001831454,0.00001380288,0.9975563,0.000004802629,0.000004477969,0.00008125728,0.0003278618,0.001099045,0.0007265045,0.00004058943,0.0001401199,0.000003441403],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991578,0.00003093402,0.00005401624,0.00001343728,7.49445e-7,0.000002213616,0.00008357666,0.000004192756,0.000653193],"genre_scores_gemma":[0.9998119,0.00001346565,0.00002440857,0.000001849142,3.244388e-7,5.791126e-7,0.00002526927,5.82122e-7,0.0001215584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2439484,"threshold_uncertainty_score":0.4850569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159947883789023,"score_gpt":0.2125381299296434,"score_spread":0.2009386510917532,"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."}}