Impact of Vertical Mixing on Summertime Net Community Production in Canadian Arctic and Subarctic Waters: Insights From In Situ Measurements and Numerical Simulations
Bibliographic record
Abstract
Abstract We present ΔO2/Ar‐based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O2 budget. Large positive mixing fluxes, exceeding 100 mmol O2 m−2 d−1, were derived in regions of strong wind‐driven mixing, such as the Labrador Sea (LS), and in the physically‐dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O2 m−2 d−1, on average) in the density‐stratified Baffin Bay, where mixing was low, and parts of the well‐mixed Hudson Strait (HS), where vertical O2 gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the LS, HS, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ∼58% of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO2/Ar‐based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under‐sampled, rapidly changing, high‐latitude waters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".