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Record W3128632267 · doi:10.1029/2020gl091403

Iron Regulation of North Atlantic Eddy Phytoplankton Productivity

2021· article· en· W3128632267 on OpenAlexaboutno aff
Thomas J. Browning, Ali A. Al‐Hashem, Mark J. Hopwood, Anja Engel, Igor M. Belkin, Ewan D. Wakefield, Tim Fischer, Eric P. Achterberg

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsBundesministerium für Bildung und ForschungNatural Environment Research CouncilSight Research UK
KeywordsOceanographyUpwellingEddyProductivityPhytoplanktonEnvironmental scienceContinental shelfLatitudeBiomass (ecology)PlanktonGeologyNutrientEcologyGeographyBiologyMeteorology

Abstract

fetched live from OpenAlex

Abstract Cyclonic ocean eddies drive upwelling of deep waters enhanced in nutrients, which can elevate phytoplankton productivity. At mid‐latitudes in the North Atlantic, satellite images show enhanced chlorophyll‐a associated with eddies. However, surface macronutrient concentrations are often not fully depleted in this region, implying enhanced macronutrient supply is not the primary control. We conducted high resolution sampling through two mid‐latitude Atlantic eddies in late spring, located 800 and 350 km east of the Newfoundland Grand Banks. Waters outside of both eddies had unused residual macronutrients, low dissolved iron, and iron‐stressed phytoplankton. Inside both eddies, plankton biomass was higher and macronutrient concentrations lower. However, full macronutrient drawdown and an absence of iron stress were only present in the eddy nearer the continental shelf. From these two examples, iron supply and proximity to shelf iron sources appear to be important factors regulating productivity and macronutrient utilization in mid‐latitude North Atlantic cyclonic eddies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.023
GPT teacher head0.251
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

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