{"id":"W3091887703","doi":"10.5194/egusphere-egu2020-3626","title":"Reconstruction of the Inshore Labrador Current using SWOT: from OI to DENKF","year":2020,"lang":"en","type":"article","venue":"","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"SWOT analysis; Sea-surface height; Geostrophic current; Current (fluid); Ocean surface topography; Data assimilation; Ocean current; Kalman filter; Geostrophic wind; Interpolation (computer graphics); Geography; Oceanography; Geology; Environmental science; Climatology; Meteorology; Altimeter; Mathematics; Computer science; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001551358,0.0004582821,0.0003309847,0.0003349472,0.0002449233,0.0004793222,0.0003293426,0.0002952571,0.001790824],"category_scores_gemma":[0.0004876609,0.0002856228,0.0003511082,0.0004709593,0.0001976573,0.0004374964,0.0003580717,0.0004457722,0.0004041154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007125023,"about_ca_system_score_gemma":0.001437127,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1725047,"about_ca_topic_score_gemma":0.1792298,"domain_scores_codex":[0.9999251,0.000006580468,0.000003944847,0.00001984711,0.00002446342,0.00002002231],"domain_scores_gemma":[0.9998717,0.000009741398,0.00001618808,0.00002567574,0.00005989314,0.00001677467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001535488,0.00008002351,0.04785578,0.00004812856,0.0001076925,0.0001417372,0.0001623518,0.8740477,0.01159588,0.001210582,0.003022603,0.06157391],"study_design_scores_gemma":[0.00003518003,0.00001731348,0.02296478,0.000009606372,0.00001875608,0.00001862921,0.00006180489,0.9711509,0.003062262,0.0002618286,0.00237859,0.00002039684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9160732,0.0001204306,0.07248276,0.0001604211,0.00009311417,0.00002978192,0.002265917,0.001650326,0.007123961],"genre_scores_gemma":[0.9703522,0.0000941753,0.02340678,0.00002652238,0.0000140273,0.00001603343,0.002956208,0.0001448129,0.00298922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8274953,"threshold_uncertainty_score":0.3430012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02973394650936827,"score_gpt":0.2139651426147801,"score_spread":0.1842311961054118,"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."}}