{"id":"W4381187381","doi":"10.1017/9781009180412.002","title":"Inverse Problems and Data Assimilation in Earth Sciences","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data assimilation; Hadamard transform; Inverse problem; Tikhonov regularization; Uniqueness; Well-posed problem; Assimilation (phonology); Computer science; Applied mathematics; Mathematics; Geography; Mathematical analysis; Meteorology; Philosophy","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":[0.0004428221,0.0009825471,0.0007327533,0.001272617,0.0006613145,0.003179511,0.0007018563,0.001625101,0.01872217],"category_scores_gemma":[0.001081125,0.0003900262,0.0006571706,0.002096509,0.00182735,0.003850139,0.001407333,0.003371621,0.0129242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008051624,"about_ca_system_score_gemma":0.0008853176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482566,"about_ca_topic_score_gemma":0.001629077,"domain_scores_codex":[0.9995837,0.00007040129,0.00001756591,0.00008215475,0.0002171606,0.00002912126],"domain_scores_gemma":[0.9996897,0.000150475,0.0000170105,0.00004128268,0.0000835773,0.00001795176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002103768,0.00004324332,0.0001908032,0.000861228,0.00002715021,0.0001134119,0.0007942783,0.003391101,0.002706694,0.4989405,0.311808,0.1811025],"study_design_scores_gemma":[0.000001596355,0.000007428439,0.000124763,0.0001390991,0.000002873409,0.0001090896,0.00005761503,0.0008198718,0.0002803278,0.05430011,0.9441511,0.000006188784],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002497206,0.2483073,0.1728126,0.01535673,0.01780939,0.0001453225,0.001277093,0.001138156,0.5406561],"genre_scores_gemma":[0.02736645,0.137521,0.1060808,0.01055842,0.007881233,0.0003122847,0.001475871,0.001205918,0.707598],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01872217,"threshold_uncertainty_score":0.06263191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113499734609363,"score_gpt":0.2196743051782845,"score_spread":0.1083243317173483,"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."}}