Assessing Wintertime Export Fluxes in the Labrador Sea using ²³⁴Th-²³⁸U Disequilibria and a Mechanistic Particle Sinking Model.
Bibliographic record
Abstract
The export efficiency of biogenic particles from the upper ocean is a crucial determinant of ocean carbon sequestration. High-latitude regions, such as the Labrador Sea, have been reported to be quite efficient at transporting carbon to the deep ocean, but the magnitude and efficiency of this carbon export vary widely between seasons. In this study, we combined observational measurements of export flux with a particle sinking model to constrain a seasonal carbon flux during winter, a time of year when the Labrador Sea is characterized by deepening convection and persistent storms. Samples were collected from 8 stations between December 2 and 23, following a Lagrangian sampling scheme that tracked an SF6-tagged water mass. POC export fluxes estimated using a steady-state 234Th-238U disequilibria approach and ranged from 1.3 mmol C m−2 d−1 to 8.6 mmol C m−2 d−1 at 100 m. However, this estimate is likely a lower limit due to the influences of physical and non-steady-state processes. The export efficiency, ratio of POC export to net primary production (NPP), varied from 17 % to 99 %, which revealed that the Labrador Sea can be quite efficient at exporting carbon in winter. This apparently higher export efficiency could be due to a time lag and seasonal decoupling of export flux and NPP. The mechanistic particle sinking model further supported this notion by revealing that smaller, slowly sinking particles play a significant role in efficiently transporting carbon in winter, which can result in such decoupling. This is the first study to show the importance of the Labrador Sea in exporting significant amounts of carbon during winter, and further research needs to be done to better constrain the overall biological carbon pump (BCP) in higher latitude seas. Significant seasonal gaps reveal the need to further constrain how the BCP functions on an annual basis. This will improve the predictive capabilities of how the BCP will respond to future climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".