{"id":"W2526485749","doi":"10.1038/srep33978","title":"Using fluorescent dissolved organic matter to trace and distinguish the origin of Arctic surface waters","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography","funders":"Narodowe Centrum Badań i Rozwoju; Universität Bremen; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; University of Washington; Strategic Research Council; Framsenteret; Norsk Polarinstitutt; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"TRACE (psycholinguistics); Dissolved organic carbon; Arctic; Environmental chemistry; Fluorescence; Organic matter; Surface water; Environmental science; Chemistry; Oceanography; Geology; Environmental engineering; Organic chemistry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009027752,0.0001168095,0.0001460507,0.00003824889,0.0003184035,0.0001118087,0.0001434625,0.00002898042,0.0008923524],"category_scores_gemma":[0.0001408167,0.00005773549,0.00004095635,0.0002407742,0.0003917664,0.0001276205,0.00003660669,0.00005558515,0.00005209723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001227977,"about_ca_system_score_gemma":0.00006373866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006526661,"about_ca_topic_score_gemma":0.0002111721,"domain_scores_codex":[0.998544,0.00004760925,0.000328474,0.0004217844,0.0003422184,0.0003159141],"domain_scores_gemma":[0.9990795,0.0001085274,0.0001637631,0.0004248215,0.00008402536,0.0001393593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001040239,0.000009568591,0.9877092,0.00002495323,0.00001007448,0.00003901501,0.0006658948,0.0001468275,0.009060624,0.000006485747,0.0004566381,0.001860299],"study_design_scores_gemma":[0.0002353248,0.00006996703,0.9765492,0.0003351273,0.00009135689,0.0006589561,0.0009645919,0.002841301,0.005766003,0.00335511,0.008657239,0.0004758317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954202,0.00003995401,0.00115043,0.0009758683,0.002055357,0.0001835004,0.00001028919,0.00001330287,0.0001510587],"genre_scores_gemma":[0.9973447,0.000003476793,0.001286245,0.00005662115,0.00003175316,2.416276e-7,0.000007870505,0.000004840282,0.00126429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01116003,"threshold_uncertainty_score":0.9770633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169407059528064,"score_gpt":0.2264493336594916,"score_spread":0.2095086277066852,"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."}}