Merging Satellite and in situ Data to Assess the Flux of Terrestrial Dissolved Organic Carbon From the Mackenzie River to the Coastal Beaufort Sea
Bibliographic record
Abstract
In response to global warming, the Arctic is undergoing rapid and unprecedented changes that alter the land-to-sea forcing in large Arctic rivers. Improving our knowledge of terrestrial dissolved organic carbon (tDOC) flux to the coastal Arctic Ocean (AO) is thus critical and timely as these changes strongly alter the biogeochemical cycles on AO shelves. In this study, we merged riverinein situtDOC concentrations with satellite ocean-color estimates retrieved at the land-marine interface of the Mackenzie Delta to make a first assessment of the tDOC export from its main outlets to the shelf. We combined tDOC and river discharge data to develop a regression model that simulated tDOC concentrations and fluxes from daily to interannual (2003–2017) time scales. We then compared the simulated satellite-derived estimates to those simulated by the model constrained byin situtDOC data only. As the satellite tDOC estimates reflect the delta effect in terms of tDOC enrichment and removal, our results inform us of how much tDOC can potentially leave the delta to reach the ocean (1.44 ± 0.14 TgC.yr−1). The chemodynamic relationships and the model suggest contrasting patterns between Shallow Bay and the two easternmost delta outlets, which can be explained by the variability in their geomorphological settings. At the seasonal scale and for all outlets, the satellite-derived tDOC export departs from the estimate based onin situtDOC data only. During the river freshet in May, the satellite-derived tDOC export is, on average, ∼15% (Shallow Bay) to ∼20% (Beluga Bay) lower than the in situ-derived estimate. This difference was the highest (−60%) in 2005 and exceeds 30% over most of the last decade, and can be explained by qualitative and quantitative differences between the tDOCin situand tDOCsatdatasets in a period when the freshet is highly variable. In contrast, in summer and fall, the satellite-derived tDOC export is higher than the in situ-derived estimate. The temporal difference between the satellite and in situ-derived export estimates suggests that predicting seasonal tDOC concentrations and fluxes from remote Arctic deltas to the coastal AO remains a challenge for assessing their impact on already changing carbon fluxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".