{"id":"W4229847860","doi":"10.5194/essdd-2-281-2009","title":"Arctic Ocean data in CARINA","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Ocean Acidification Effects and Responses","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration; Norges Forskningsråd","keywords":"Alkalinity; Arctic; Environmental science; The arctic; Silicate; Nitrate; Carbon fibers; Nutrient; Linear regression; Data set; Quality (philosophy); Dissolved organic carbon; Total inorganic carbon; Oceanography; Computer science; Geology; Mathematics; Statistics; Chemistry; Carbon dioxide; Ecology; Algorithm; Biology","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":[0.001223654,0.0005991942,0.0005278619,0.002702927,0.0009782726,0.001823305,0.000619523,0.0004097981,0.009424913],"category_scores_gemma":[0.002739103,0.000308769,0.0003196546,0.004220972,0.0002391632,0.0004883413,0.001201357,0.0004906915,0.00539665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001916574,"about_ca_system_score_gemma":0.003654136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.271699,"about_ca_topic_score_gemma":0.2929953,"domain_scores_codex":[0.9987779,0.0001594703,0.000102214,0.0003842009,0.0004247986,0.0001514078],"domain_scores_gemma":[0.9966676,0.0002317228,0.0004018417,0.0006110058,0.001878637,0.000209218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003555363,0.0001632165,0.2440033,0.001297242,0.0006064606,0.0007946505,0.002070871,0.009409578,0.00539585,0.008671176,0.5837698,0.1402624],"study_design_scores_gemma":[0.00006078035,0.00004860031,0.1696184,0.0001371879,0.00007452293,0.0001129282,0.0005697438,0.001949775,0.003208508,0.0004997627,0.8236722,0.00004762186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1056938,0.0018472,0.00182016,0.0007262399,0.0007783722,0.000137906,0.8303968,0.00146276,0.05713667],"genre_scores_gemma":[0.1610849,0.0005977735,0.005453933,0.0002561321,0.0001413735,0.0002582626,0.800607,0.0004244903,0.0311761],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.271699,"threshold_uncertainty_score":0.540235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04281662277675963,"score_gpt":0.2687266214201263,"score_spread":0.2259099986433666,"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."}}