{"id":"W4313458928","doi":"10.1190/tle42010061.1","title":"EcoSeis: A novel acquisition method for optimizing seismic resolution while minimizing environmental footprint","year":2023,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cenovus Energy (Canada); Quadrise Canada Corporation (Canada); Stantec (Canada); Alberta Environment and Protected Areas","funders":"","keywords":"Footprint; Data acquisition; Computer science; Grid; Inversion (geology); Remote sensing; Data processing; Resource (disambiguation); Oil field; Field (mathematics); Data mining; Geology; Petroleum engineering; Seismology; Database","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":[0.001071441,0.0001288151,0.0001377978,0.0001325255,0.0004528678,0.00006761224,0.0002454068,0.00006334816,0.00007774514],"category_scores_gemma":[0.00003068478,0.0001008476,0.00009489982,0.0001854645,0.00006062039,0.0001296034,0.00003120043,0.0001411479,0.0003708375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002628041,"about_ca_system_score_gemma":0.00001619697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003281212,"about_ca_topic_score_gemma":0.000004592206,"domain_scores_codex":[0.9989686,0.00007094578,0.0001785939,0.000268806,0.0001511634,0.0003618732],"domain_scores_gemma":[0.9993015,0.0003434131,0.00008147804,0.0002106335,0.000009077269,0.00005389824],"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":[0.0002560626,0.00006529318,0.002745506,0.0001104082,0.0001074601,0.000008967189,0.006335619,0.1055857,0.04201907,0.0003148309,0.1679141,0.674537],"study_design_scores_gemma":[0.0002650453,0.00007392228,0.001702371,0.00004486702,0.00002489993,0.00002005628,0.0008481831,0.9120901,0.01039511,0.0008132024,0.07355195,0.0001702328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2194329,0.0004942293,0.7598442,0.009168573,0.001519624,0.001051927,0.0002613897,0.001388589,0.006838539],"genre_scores_gemma":[0.9240155,0.00008385952,0.07142945,0.00227311,0.0003336564,0.00001642376,0.0002589647,0.00001452691,0.001574508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8065045,"threshold_uncertainty_score":0.4766489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0394554376128327,"score_gpt":0.2635866968190155,"score_spread":0.2241312592061828,"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."}}