Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: Interannual variability and implications of climate change
Bibliographic record
Abstract
Photomineralization of terrigenous dissolved organic matter (tDOM) in the Arctic Ocean is limited by persistent sea ice cover that reduces the amount of ultraviolet (UV) radiation reaching the underlying water column. UV‐dependent processes are likely to accelerate as a result of shrinking sea ice extent and decreasing ice thickness caused by climatic warming over this region. In this study, we made the first quantitative estimates of photomineralization of tDOM in a coastal Arctic ecosystem under current and future sea ice regimes. We used an optical‐photochemical coupled model incorporating water column optics and experimental measurements of photoproduction of dissolved inorganic carbon (DIC), the main carbon product of DOM photochemistry. Apparent quantum yields of DIC photoproduction were determined on water samples from the Mackenzie River estuary, the Mackenzie Shelf, and Amundsen Gulf. UV irradiances just below the sea surface were estimated by combining satellite backscattered and passive microwave radiance measurements with a radiative transfer model. The mean annual DIC photoproduction between 1979 and 2003 was estimated as 66.5 ± 18.5 Gg carbon in the surface waters of the southeastern Beaufort Sea, where UV absorption is dominated by chromophoric dissolved organic matter discharged by the Mackenzie River. This value is equivalent to 10% of bacterial respiration rates, 8% of new primary production rates and 2.8 ± 0.6% of the 1.3 Tg of dissolved organic carbon (DOC) discharged annually by the Mackenzie River into the area. During periods of reduced ice cover such as 1998, the latter value could rise to 5.1% of the annual riverine DOC discharge. Under an ice‐free scenario, the model predicted that 150.5 Gg of DIC would be photochemically produced, mineralizing 6.2% of the DOC input from the Mackenzie River. These results show that the predicted trend of ongoing contraction of sea ice cover will greatly accelerate the photomineralization of tDOM in Arctic surface 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".