{"id":"W2124122180","doi":"10.1111/j.1365-246x.2011.05131.x","title":"A distribution-based parametrization for improved tomographic imaging of solute plumes","year":2011,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Parametrization (atmospheric modeling); Plume; Tomography; Tikhonov regularization; Inverse problem; Synthetic data; Geology; Tomographic reconstruction; Algorithm; Physics; Mathematics; Optics; Meteorology; Mathematical analysis","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":[0.0005135686,0.0006188623,0.0004237122,0.0004896933,0.0002476467,0.0009188604,0.0006204474,0.0006610227,0.0007362078],"category_scores_gemma":[0.001993461,0.0004156164,0.0005543517,0.0005165213,0.000754217,0.001112771,0.0007996232,0.001086864,0.0003075428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006387472,"about_ca_system_score_gemma":0.0006214784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001811046,"about_ca_topic_score_gemma":0.0021812,"domain_scores_codex":[0.9998384,0.00004705682,0.00001136394,0.00004111271,0.00004500675,0.0000170561],"domain_scores_gemma":[0.9996694,0.0001579687,0.00005828122,0.00005674229,0.0000391282,0.00001854619],"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":[0.0001239159,0.00008038148,0.001726878,0.0001417791,0.00002629937,0.000200872,0.0002229755,0.6422356,0.2018016,0.05466055,0.001119444,0.09765967],"study_design_scores_gemma":[0.000006826418,0.00001278714,0.0003816572,0.000005250395,0.000004557978,0.00006868622,0.00001047537,0.9868731,0.00677778,0.004210728,0.001634402,0.00001366824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01146857,0.00005055948,0.9876541,0.0001299111,0.000005385076,0.00001725617,0.00005308811,0.0001889323,0.0004321843],"genre_scores_gemma":[0.3305714,0.0002768256,0.666625,0.000122961,0.00003173637,0.0001693828,0.000326798,0.0003561042,0.001519779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001811046,"threshold_uncertainty_score":0.0046345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713991662650884,"score_gpt":0.2505548086523726,"score_spread":0.2334148920258637,"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."}}