The Impact of Initial Tracer Profile on the Exchange and On‐Shelf Distribution of Tracers Induced by a Submarine Canyon
Bibliographic record
Abstract
Abstract Submarine canyons enhance cross‐shelf mass exchanges, which are a key component of on‐shelf nutrient budgets and biogeochemical cycles. Previous studies assume that canyon‐induced tracer flux onto the shelf only depends on canyon‐induced water upwelling. This paper investigates the validity of this dependence for nutrients, carbon, and dissolved gasses. To estimate the canyon‐induced tracer upwelling flux and its spatial distribution on the shelf, we performed numerical experiments simulating an upwelling event near an idealized canyon, adding 10 passive tracers with initial profiles representing nutrients, carbon, and dissolved gasses. This paper presents a scaling estimate for canyon‐induced tracer upwelling and for the on‐shelf distribution of a given tracer. We find that tracer upwelling depends on the vertical local mean of the initial vertical tracer gradient within the canyon, the depth of upwelling and the upwelling flux. We identify a pool of low oxygen and high nutrient concentration, methane, dissolved inorganic carbon, and total alkalinity on the shelf bottom, downstream of the canyon. The downstream extension of the pool of low oxygen water depends on the onshore flux of water through the canyon and the oxygen profile. This canyon‐induced distribution of tracers has the potential to impact demersal and benthic ecosystems by lowering dissolved oxygen levels and spreading corrosive waters along the shelf.
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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 | 0.000 | 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".