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Record W3005674190

Iron Supply and Demand in an Antarctic Shelf Ecosystem

2016· article· en· W3005674190 on OpenAlexaff
Dennis J. McGillicuddy, Peter N. Sedwick, Michael S. Dinniman, Kevin R. Arrigo, Thomas S. Bibby, B. J. W. Greenan, Eileen E. Hofmann, John M. Klinck, Walker O Smith, Stefanie Mack, Chris M. Marsay, B. M. Sohst, Gert L. van Dijken

Bibliographic record

VenueW&M Publish (College of William & Mary) · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsOceanographyEnvironmental sciencePhytoplanktonPhotic zoneLead (geology)EcosystemProductivitySea iceMarine ecosystemPrimary productivityIron fertilizationNew productionGeologyEcology
DOInot available

Abstract

fetched live from OpenAlex

The Ross Sea sustains a rich ecosystem and is the most productive sector of the Southern Ocean. Most of this production occurs within a polynya during the November-February period, when the availability of dissolved iron (dFe) is thought to exert the major control on phytoplankton growth. Here we combine new data on the distribution of dFe, high-resolution model simulations of ice melt and regional circulation, and satellite-based estimates of primary production to quantify iron supply and demand over the Ross Sea continental shelf. Our analysis suggests that the largest sources of dFe to the euphotic zone are wintertime mixing and melting sea ice, with a lesser input from intrusions of Circumpolar Deep Water and a small amount from melting glacial ice. Together these sources are in approximate balance with the annual biological dFe demand inferred from satellite-based productivity algorithms, although both the supply and demand estimates have large uncertainties.

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)0.001
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 score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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