{"id":"W4414110681","doi":"10.1109/access.2025.3608240","title":"Technical Advancements of Laterally Constrained Inversion for Geophysical Datasets","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Inversion (geology); Classification of discontinuities; A priori and a posteriori; Inverse problem; Ground-penetrating radar; Synthetic data; Flexibility (engineering); Regularization (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005613599,0.001347,0.001005557,0.002818675,0.0004993922,0.00254088,0.002189734,0.001333921,0.003135469],"category_scores_gemma":[0.01252002,0.0009520017,0.001620135,0.003987268,0.001496852,0.003070116,0.002481078,0.003157803,0.001744247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032662,"about_ca_system_score_gemma":0.002858962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002859831,"about_ca_topic_score_gemma":0.002286958,"domain_scores_codex":[0.9971331,0.0008683693,0.0002796339,0.0005862108,0.001051006,0.00008165447],"domain_scores_gemma":[0.9942088,0.003181967,0.0003487016,0.0007641317,0.001417529,0.00007889801],"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":[0.00007614066,0.00004204742,0.001760845,0.005126665,0.0002199934,0.0002638048,0.0004034988,0.0466047,0.02606016,0.1039097,0.008525431,0.807007],"study_design_scores_gemma":[0.00004805119,0.000166482,0.003214718,0.00279567,0.0002726762,0.00119694,0.0003701383,0.3166864,0.03559044,0.1289487,0.5103471,0.0003626752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001644122,0.01544536,0.9761633,0.0008701775,0.0002229636,0.00009101687,0.0004012679,0.0006075438,0.00455415],"genre_scores_gemma":[0.0272554,0.04113081,0.9262434,0.0005704841,0.0006588911,0.0004258956,0.00144846,0.0006042099,0.001662475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005613599,"threshold_uncertainty_score":0.02968788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976794480580293,"score_gpt":0.3010857917426266,"score_spread":0.2813178469368237,"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."}}