{"id":"W1996230998","doi":"10.1016/j.csr.2006.10.011","title":"Suppressing bias and drift of coastal circulation models through the assimilation of seasonal climatologies of temperature and salinity","year":2007,"lang":"en","type":"article","venue":"Continental Shelf Research","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Climatology; Baroclinity; Hindcast; Wavenumber; Annual cycle; Data assimilation; Stratification (seeds); Upwelling; Environmental science; Temperature salinity diagrams; Ocean current; Wave model; Ocean general circulation model; Ocean dynamics; Geology; Oceanography; Atmospheric sciences; Meteorology; General Circulation Model; Salinity; Climate change; Geography; Physics","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":[0.0009602746,0.0004316451,0.000297662,0.0002228143,0.0002941627,0.0006149521,0.000294947,0.000396267,0.0003108176],"category_scores_gemma":[0.00470837,0.0003788621,0.0002655058,0.0002760773,0.0001906962,0.0006727345,0.0004858658,0.000563962,0.0001711038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002887771,"about_ca_system_score_gemma":0.001104381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01563904,"about_ca_topic_score_gemma":0.02663033,"domain_scores_codex":[0.9998614,0.00004299344,0.00001060822,0.00003088158,0.00003039701,0.00002376002],"domain_scores_gemma":[0.9993998,0.0002227493,0.00008831841,0.0001012542,0.0001539434,0.00003399676],"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":[0.0009131847,0.0002749343,0.06985103,0.00008760455,0.0004861452,0.0001023621,0.0003560879,0.6640286,0.07683651,0.006265805,0.001962614,0.178835],"study_design_scores_gemma":[0.0000405408,0.0000336889,0.007296144,0.000005176549,0.00005185475,0.00001422204,0.00001789845,0.983869,0.007367966,0.0007982154,0.0004944173,0.00001088283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164687,0.0002779133,0.08079378,0.0003595673,0.000198196,0.00001340913,0.0001448819,0.0005628168,0.001180683],"genre_scores_gemma":[0.9773059,0.0001958936,0.02104477,0.00006954792,0.00005561685,0.000007557527,0.0002073003,0.0001035886,0.001009832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01563904,"threshold_uncertainty_score":0.03109604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07186709160249466,"score_gpt":0.3141957363936575,"score_spread":0.2423286447911628,"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."}}