{"id":"W2020219450","doi":"10.1046/j.1365-246x.2003.01766.x","title":"Fast inversion of large-scale magnetic data using wavelet transforms and a logarithmic barrier method","year":2003,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":425,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Conjugate gradient method; Wavelet; Wavelet transform; Algorithm; Mathematics; Logarithm; Coefficient matrix; Matrix (chemical analysis); Solver; Stationary wavelet transform; Wavelet packet decomposition; Computer science; Mathematical optimization; Mathematical analysis; Artificial intelligence; Eigenvalues and eigenvectors; Physics","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":[0.0009546106,0.0001210071,0.0001943164,0.0001395354,0.0001249736,0.0001678882,0.000883968,0.0000517192,0.00007502652],"category_scores_gemma":[0.00009549021,0.00009966071,0.0000780082,0.0001704274,0.0000570365,0.0009113153,0.0002384309,0.0002927674,0.000005608254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003200006,"about_ca_system_score_gemma":0.00009241545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001584927,"about_ca_topic_score_gemma":0.000001871757,"domain_scores_codex":[0.9984925,0.0002413684,0.0002982796,0.0002829274,0.0004633699,0.0002215275],"domain_scores_gemma":[0.9991572,0.0001301158,0.0001184965,0.0002877579,0.0001752156,0.0001311463],"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":[0.0001992668,0.0008486598,0.000636838,0.00006468715,0.0003082912,0.0003359922,0.004597967,0.0006148599,0.3370153,0.05570566,0.001143071,0.5985293],"study_design_scores_gemma":[0.002019906,0.0001829288,0.001068598,0.00007552919,0.00004206599,0.00105818,0.0001360758,0.9320898,0.01670616,0.04143481,0.004929617,0.0002562984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05121738,0.00007992516,0.9471185,0.0003344506,0.0004495001,0.00004789955,0.00002492015,0.00001005476,0.0007174281],"genre_scores_gemma":[0.1899157,0.00003217602,0.809163,0.0003683218,0.0001837215,6.193487e-7,0.000006453066,0.000009998805,0.0003199471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.931475,"threshold_uncertainty_score":0.4064046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818622910144782,"score_gpt":0.3187276320347983,"score_spread":0.2905414029333505,"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."}}