The Influence of Sea Ice Cover and Atlantic Water Advection on Annual Particle Export North of Svalbard
Bibliographic record
Abstract
Abstract The Arctic Ocean north of Svalbard has recently experienced large sea ice losses and the increasing prominence of Atlantic water (AW) advection. To investigate the impact of these ongoing changes on annual particle export, two moorings with sequential sediment traps were deployed in ice‐free and seasonally ice‐covered waters on the shelf north (NSv) and east (ESv) of Svalbard, collecting sinking particles nearly continuously from October 2017 to October 2018. Vertical export of particulate organic carbon (POC), total particulate matter (TPM), planktonic protists, chlorophyll a , and zooplankton fecal pellets were measured, and swimmers were quantified and identified. Combined with sensor data from the moorings, these time‐series measurements provided a first assessment of the factors influencing particle export in this region of the Arctic Ocean. Higher annual TPM and POC fluxes at the ice‐free NSv site were primarily driven by the advection of AW, higher grazing by large copepods, and a wind‐induced mixing event during winter. Higher diatom fluxes were observed during spring in the presence of sea ice at the ESv site. Along with sea ice cover, regional differences in AW advection and the seasonal presence of grazers played a prominent role in the biological carbon pump along the continental shelf off Svalbard.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".