{"id":"W4417268123","doi":"10.1103/8h9r-rvhw","title":"Constraining <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:msub> <mml:mi>f</mml:mi> <mml:mrow> <mml:mi>N</mml:mi> <mml:mi>L</mml:mi> </mml:mrow> </mml:msub> </mml:math> using the large-scale modulation of small-scale statistics","year":2025,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Perimeter Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities; Wisconsin Alumni Research Foundation; University of Wisconsin-Madison; U.S. Department of Energy; Innovation, Science and Economic Development Canada; Government of Canada; National Science Foundation","keywords":"Modulation (music); Noise (video); Frequency modulation; Phase modulation; Statistical analysis; Work (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00140865,0.0006796514,0.0004986656,0.0014334,0.0004554539,0.002049119,0.0008511137,0.000702153,0.01780847],"category_scores_gemma":[0.01214795,0.0005774369,0.0005828069,0.001493104,0.0004505803,0.002070772,0.0008754594,0.001179198,0.005541139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008197352,"about_ca_system_score_gemma":0.002492181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01609568,"about_ca_topic_score_gemma":0.0244968,"domain_scores_codex":[0.9995502,0.0001883951,0.00002560533,0.00009940516,0.0001087725,0.00002757671],"domain_scores_gemma":[0.9979802,0.00141359,0.00016318,0.0002229241,0.0001794532,0.0000407621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001385132,0.0001014564,0.01041252,0.000593472,0.0001829798,0.0002388003,0.0002392469,0.2537073,0.01303689,0.3349418,0.1105279,0.275879],"study_design_scores_gemma":[0.000045733,0.00003006884,0.004314587,0.0001294494,0.00005101436,0.00008984194,0.0001885749,0.7298014,0.01195714,0.1549163,0.0984221,0.00005379647],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02097001,0.0005604625,0.9393998,0.0017011,0.0001738378,0.00006333695,0.004546213,0.003090243,0.02949494],"genre_scores_gemma":[0.4505646,0.00185435,0.5136822,0.000591663,0.000276166,0.0003325717,0.01234401,0.004089323,0.01626519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01780847,"threshold_uncertainty_score":0.05957526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057685256609076,"score_gpt":0.3067483780613925,"score_spread":0.2861715254953018,"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."}}