{"id":"W1952139873","doi":"10.1080/1755876x.2015.1022329","title":"The Ocean Reanalyses Intercomparison Project (ORA-IP)","year":2015,"lang":"en","type":"article","venue":"Journal of Operational Oceanography","topic":"Climate variability and models","field":"Environmental Science","cited_by":310,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Japan Agency for Marine-Earth Science and Technology; European Commission; National Oceanic and Atmospheric Administration; Sight Research UK; National Centre for Earth Observation; Natural Environment Research Council; Met Office; Ministry of Education, Culture, Sports, Science and Technology; Department for Environment, Food and Rural Affairs, UK Government; National Aeronautics and Space Administration","keywords":"Data assimilation; Environmental science; Climatology; Ocean heat content; Ocean observations; Meteorology; Deep sea; Ocean current; Oceanography; Geology; Geography","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":[0.009877845,0.002289238,0.002337185,0.002993136,0.0009082897,0.002099958,0.002396608,0.001631789,0.004217158],"category_scores_gemma":[0.008563325,0.0005967586,0.001598191,0.01030749,0.0005993456,0.002286119,0.003801106,0.002718485,0.002189755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009807523,"about_ca_system_score_gemma":0.007764756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02025977,"about_ca_topic_score_gemma":0.009560862,"domain_scores_codex":[0.9973353,0.0008612653,0.000253523,0.0005289615,0.0008082564,0.0002127792],"domain_scores_gemma":[0.9944588,0.0008138971,0.0008845463,0.001509752,0.001950908,0.0003820892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001943552,0.0004383698,0.03176976,0.002760337,0.004718091,0.0005018209,0.000512583,0.04979719,0.01698779,0.03742539,0.2402918,0.6128533],"study_design_scores_gemma":[0.001451361,0.0005280277,0.09461889,0.0005770039,0.001516095,0.0002051794,0.0002977403,0.05240953,0.01407768,0.03445466,0.7995654,0.0002985037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08294442,0.0198104,0.1919863,0.0110318,0.009514627,0.001935164,0.6021944,0.01516124,0.06542166],"genre_scores_gemma":[0.2103251,0.01205551,0.3737577,0.002409632,0.001872553,0.004189449,0.3788561,0.003754859,0.01277923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02025977,"threshold_uncertainty_score":0.05223966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0538028320237779,"score_gpt":0.3060598532128152,"score_spread":0.2522570211890373,"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."}}