{"id":"W3156874898","doi":"10.31223/x52603","title":"A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Polar Programs; Office of Polar Programs; Natural Sciences and Engineering Research Council of Canada; Compute Canada; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Dissolved organic carbon; Carbon fibers; Environmental science; Deep sea; Ocean current; Oceanography; Latitude; Carbon cycle; Indian ocean; Climatology; Geology; Atmospheric sciences; Computer science; Algorithm","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":[0.0005534194,0.0005422462,0.0003692959,0.0003979224,0.0002555667,0.0005002341,0.0005001761,0.0006881977,0.001663387],"category_scores_gemma":[0.001832127,0.0003084774,0.0004509316,0.0004831448,0.0003143972,0.0007379664,0.0005784659,0.001015567,0.00042267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006188771,"about_ca_system_score_gemma":0.001083657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278329,"about_ca_topic_score_gemma":0.01304572,"domain_scores_codex":[0.9999135,0.00001055843,0.0000043228,0.00003757733,0.00001340231,0.00002070293],"domain_scores_gemma":[0.9996183,0.0001349533,0.00005624482,0.00004760539,0.0001076892,0.00003519936],"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":[0.0001547516,0.0001104873,0.02933925,0.00006301863,0.0001947866,0.00007459101,0.00003665374,0.873834,0.004493361,0.002819499,0.006303237,0.08257645],"study_design_scores_gemma":[0.000008967313,0.00001201957,0.003761908,0.000005981808,0.000009565467,0.000009925873,0.000007050322,0.9927244,0.0007071682,0.00221579,0.0005320472,0.00000529389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7087897,0.001166183,0.2759905,0.002156026,0.0002822691,0.0000361584,0.00453872,0.001914449,0.005125998],"genre_scores_gemma":[0.9647333,0.0002427805,0.02775682,0.0001871292,0.0000811565,0.00003642092,0.003964981,0.00005786249,0.002939513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01278329,"threshold_uncertainty_score":0.02541775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084430868595335,"score_gpt":0.2098649730137131,"score_spread":0.1990206643277597,"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."}}