{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005144876,0.0005660661,0.0002864086,0.0005424023,0.0001913459,0.000371481,0.000659868,0.0002396775,0.002605939],"category_scores_gemma":[0.001473939,0.0002504464,0.0001668886,0.0004561787,0.0002995075,0.000787305,0.0005875925,0.000401443,0.0006442589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329488,"about_ca_system_score_gemma":0.0004862848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001403766,"about_ca_topic_score_gemma":0.005383099,"domain_scores_codex":[0.9997,0.00004081164,0.00001723695,0.00005614662,0.0001589698,0.00002683854],"domain_scores_gemma":[0.9995204,0.0001259222,0.00005820594,0.00007224102,0.0001984987,0.0000247073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005021944,0.0001801865,0.00393272,0.0001106963,0.00004631046,0.0001127435,0.0001675827,0.04177375,0.24386,0.005979852,0.004283547,0.6990505],"study_design_scores_gemma":[0.00007016541,0.000158033,0.002816512,0.0000103801,0.00002048295,0.0002080061,0.00006935033,0.8110212,0.1727808,0.001631146,0.01117373,0.0000402611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04238159,0.0000403615,0.9541772,0.00006933277,0.00002357163,0.00003654517,0.0001454755,0.002043603,0.001082282],"genre_scores_gemma":[0.1037857,0.00003850327,0.8938546,0.00002775773,0.0000158737,0.00004963466,0.0003019689,0.0002954538,0.001630632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002605939,"threshold_uncertainty_score":0.008717775,"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."}}