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Record W3080785158 · doi:10.1029/2020gb006592

Ironing Out Fe Residence Time in the Dynamic Upper Ocean

2020· article· en· W3080785158 on OpenAlexafffund
Erin Black, Markus Kienast, Nolwenn Lemaitre, Phoebe J. Lam, Robert F. Anderson, Hélène Planquette, F. Planchon, Ken O. Buesseler

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersVrije Universiteit BrusselOcean Frontier InstituteFonds Wetenschappelijk OnderzoekNational Aeronautics and Space AdministrationAgence Nationale de la RechercheNational Science Foundation
KeywordsIron fertilizationGeotracesResidence time (fluid dynamics)Biogeochemical cycleSediment trapEnvironmental scienceOceanographyResidenceCyclingOcean currentDeep seaSedimentCarbon cycleClimatologyGeologySeawaterWater columnPhytoplanktonGeographyEcosystemChemistryEnvironmental chemistryNutrientEcology

Abstract

fetched live from OpenAlex

Abstract Although iron availability has been shown to limit ocean productivity and influence marine carbon cycling, the rates of processes driving iron's removal and retention in the upper ocean are poorly constrained. Using 234 Th‐ and sediment‐trap data, most of which were collected through international GEOTRACES efforts, we perform an unprecedented observation‐based assessment of iron export from and residence time in the upper ocean. The majority of these new residence time estimates for total iron in the surface ocean (0–250 m) fall between 10 and 100 days. The upper ocean residence time of dissolved iron, on the other hand, varies and cycles on sub‐annual to annual timescales. Collectively, these residence times are shorter than previously thought, and the rates and timescales presented here will contribute to ongoing efforts to integrate iron into global biogeochemical models predicting climate and carbon dioxide sequestration in the ocean in the 21st century and beyond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2020
Admission routes2
Has abstractyes

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