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Record W1506263099 · doi:10.1029/2008gb003326

British Columbian continental shelf as a source of dissolved iron to the subarctic northeast Pacific Ocean

2009· article· en· W1506263099 on OpenAlexafffundabout
Jay T. Cullen, Marina Chong, Debby Ianson

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans CanadaUniversity of Victoria
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsOceanographyContinental shelfGeologyUpwellingDownwellingIsopycnalSubarctic climateSubmarine pipelineWater mass

Abstract

fetched live from OpenAlex

The distribution of dissolved (<0.4 μ m) iron (Fe) across the continental shelf and slope of Queen Charlotte Sound on the west coast of Canada was examined to estimate the potential of these waters as a source of Fe to the Fe‐limited waters of the subarctic northeast Pacific. Iron profiles obtained in shelf, slope, and offshore waters demonstrate decreasing concentrations of Fe with distance from the continent. Within 50 m of the shelf sediments dissolved Fe concentrations were 5.3 ± 0.3 nM. This signal was detected, although attenuated by 80%, along the isopycnal surface at offshore stations 40–50 km seaward of the shelf break, strongly suggesting cross‐shelf transport of an Fe‐rich plume originating in low dissolved oxygen (<3 ml L −1 , <130 μ mol kg −1 ) waters in subsurface water over the continental shelf. Several physical mechanisms that may cause these Fe‐enriched waters to advect offshore in this region (i.e., tidal currents and Ekman transport in the bottom boundary layer, coastal downwelling/relaxation from upwelling, and the formation of anticyclonic, westward‐propagating, coastal eddies) are discussed. We suggest that strong tidal currents over broad continental shelves may play a key role in Fe supply to ocean basins.

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 score0.067
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)0.0010.000

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations63
Published2009
Admission routes3
Has abstractyes

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