Pelagic primary productivity and upper ocean nutrient dynamics across Subarctic and Arctic Seas
Bibliographic record
Abstract
Phytoplankton and nutrient dynamics were investigated during the 2007 and 2008 summers in the euphotic zone of five broad domains across Subarctic and Arctic Seas: the Eastern Subarctic North Pacific Ocean, ESNP; Bering and Chukchi Seas, BE‐CH; Beaufort Sea and Canada Basin, BS‐CB; Canadian Arctic Archipelago, CAA; and Baffin Bay and Labrador Sea, BB‐LS. Average concentrations of nutrients (NO3−, NH4+, Si(OH)4, and PO43−) decreased markedly from west to east, with minima in NO3− and NH4+ in surface BS‐CB waters, but relatively invariant urea‐N concentrations across the entire region. In the BS‐CB domain, low uptake rates of nitrate (ρNO3−) and ammonium (ρNH4+) were exceeded by uptake of urea (ρUrea‐N). Whereas average ρNO3− was highest in the BE‐CH domain, ρUrea‐N was maximal in BB‐LS. Average depth‐integrated f‐ratios ranged from 0.27 in the BS‐CB domain to 0.57 in BE‐CH, while chlorophyll a (chl a) and primary productivity (ρC) were highest in BE‐CH and BB‐LS, and consistently low in the BS‐CB domain. The >5 µm phytoplankton fraction dominated ρC and ρNO3− in the BE‐CH and CAA domains, whereas ESNP and BS‐CB were dominated by the <5 µm fraction. In the BB‐LS domain, the larger cells were responsible for ∼50% of ρC, ρNO3−, and ρUrea‐N. This study highlights the contrast in ice‐corrected average new production between the BE‐CH (396 mg C m−2 d−1) and BS‐CB (5.50 mg C m−2 d−1) domains in summer, and the larger contribution of urea‐N uptake to total N uptake in central and eastern regions where NO3− concentrations were lower.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".