{"id":"W2053231967","doi":"10.1190/1.2821940","title":"Benefiting from 3D AVO by using adaptive supergathers","year":2007,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"ARC Resources (Canada)","funders":"","keywords":"Geology","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":[0.0002561297,0.0006062337,0.0003772486,0.0005358576,0.000293273,0.0005201254,0.0005758505,0.0004476906,0.002304401],"category_scores_gemma":[0.001168562,0.0003493084,0.0003107391,0.000716711,0.0003385999,0.001160511,0.0009083758,0.0005785452,0.0006851876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271616,"about_ca_system_score_gemma":0.0003240377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00153945,"about_ca_topic_score_gemma":0.004052985,"domain_scores_codex":[0.9998778,0.00003022101,0.000005955401,0.00002238061,0.00004766441,0.00001599296],"domain_scores_gemma":[0.9995939,0.0001765792,0.00004221468,0.00008896372,0.00007412201,0.00002422785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003824506,0.00008207891,0.004629794,0.0001220608,0.00006933153,0.0003259703,0.0003877715,0.1574132,0.2685811,0.01823559,0.00341583,0.5463549],"study_design_scores_gemma":[0.0000256406,0.00004609353,0.001711076,0.00001292348,0.00001859089,0.000173233,0.00007493643,0.9427396,0.03015421,0.01528169,0.009723796,0.00003819444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04017324,0.00007262866,0.9549336,0.0001492922,0.0000468629,0.00002279416,0.0001109354,0.0015127,0.002977849],"genre_scores_gemma":[0.280875,0.0001547078,0.7160189,0.0001209027,0.00005407886,0.00006760916,0.0003656901,0.0003102139,0.002032853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002304401,"threshold_uncertainty_score":0.007709026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03092494241837078,"score_gpt":0.2385845885978981,"score_spread":0.2076596461795273,"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."}}