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Record W2022626025 · doi:10.1029/2006jc004055

Iron supply to the western subarctic Pacific: Importance of iron export from the Sea of Okhotsk

2007· article· en· W2022626025 on OpenAlexaff
Jun Nishioka, Tsuneo Ono, Hiroaki Saito, Takeshi Nakatsuka, Shigenobu Takeda, Takeshi Yoshimura, Koji Suzuki, Kenshi Kuma, Shigeto Nakabayashi, Daisuke Tsumune, Humio Mitsudera, William K. Johnson, Atsushi Tsuda

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans Canada
FundersJST-Mirai Program
KeywordsSubarctic climateBiogeochemical cycleOceanographyGeotracesEnvironmental scienceSurface waterPhytoplanktonNutrientMixed layerGeologySeawaterEnvironmental chemistryChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Iron is an essential nutrient and plays an important role in the control of phytoplankton growth (Martin et al., 1989). Atmospheric dust has been thought to be the most important source of iron, supporting annual biological production in the western subarctic Pacific (WSP) (Duce and Tindale, 1991; Moore et al., 2002). We argue here for another source of iron to the WSP. We found extremely high concentrations of dissolved and particulate iron in the Okhotsk Sea Intermediate Water (OSIW) and the North Pacific Intermediate Water (NPIW), and water ventilation processes in this region probably control the transport of iron through the intermediate water layer from the continental shelf of the Sea of Okhotsk to wide areas of the WSP. Additionally, our time series data in the Oyashio region of the WSP indicate that the pattern of seasonal changes in dissolved iron concentrations in the surface‐mixed layer was similar to that of macronutrients, and that deep vertical water mixing resulted in higher winter concentrations of iron in the surface water of this region. The estimated dissolved iron supply from the iron‐rich intermediate waters to the surface waters in the Oyashio region was comparable to or higher than the reported atmospheric dust iron input and thus a major source of iron to these regions. Our data suggest that the consideration of this source of iron is essential in our understanding of spring biological production and biogeochemical cycles in the western subarctic Pacific and the role of the marginal sea.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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.

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

Citations224
Published2007
Admission routes1
Has abstractyes

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