{"id":"W4382135445","doi":"10.3390/min13070851","title":"3D Focusing Inversion of Full Tensor Magnetic Gradiometry Data with Gramian Regularization","year":2023,"lang":"en","type":"article","venue":"Minerals","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vale (Canada)","funders":"Vale Canada Limited","keywords":"Inversion (geology); Magnetization; Algorithm; Gramian matrix; Remanence; Computer science; Physics; Computational physics; Nuclear magnetic resonance; Geology; Magnetic field; Quantum mechanics; Seismology","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":[0.0003524075,0.0005399375,0.0002563992,0.0006136842,0.0002018613,0.0004643049,0.0003715239,0.0004176483,0.0008557621],"category_scores_gemma":[0.001267073,0.0002488652,0.000591305,0.0005801777,0.0003179444,0.000509271,0.0005406564,0.000429107,0.0002640575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003157128,"about_ca_system_score_gemma":0.001013498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004847778,"about_ca_topic_score_gemma":0.007874859,"domain_scores_codex":[0.9998676,0.00003479297,0.000009088866,0.00002155899,0.00005523541,0.00001184349],"domain_scores_gemma":[0.9996938,0.0001180936,0.00004898478,0.00005857222,0.00006704703,0.00001359283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001226224,0.00007963479,0.003560393,0.0001859336,0.0001334795,0.0002130845,0.0003459718,0.5442827,0.1829356,0.01706426,0.001876067,0.2492003],"study_design_scores_gemma":[0.000004427689,0.00001878941,0.0009242391,0.000004581087,0.00000731491,0.00007264748,0.00001891335,0.9811897,0.01411743,0.002261559,0.001362826,0.00001757003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02974171,0.00003393834,0.9686123,0.0000689073,0.000008737851,0.00001806959,0.0001346887,0.0006727862,0.0007088427],"genre_scores_gemma":[0.2162838,0.00009781137,0.782115,0.00004773209,0.00001095904,0.00005327902,0.0004381925,0.0001855169,0.0007675795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004847778,"threshold_uncertainty_score":0.009639084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03475954718600004,"score_gpt":0.2502987376972985,"score_spread":0.2155391905112984,"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."}}