Control of Biological Exposure to UV Radiation in the Arctic Ocean: Comparison of the Roles of Ozone and Riverine Dissolved Organic Matter
Bibliographic record
Abstract
Reports of severe stratospheric ozone depletion over the Arctic have heightened concern about the potential impact of rising ultraviolet-B (UV-B) radiation on north polar aquatic ecosystems. Our optical measurements and modelling results indicate that the ozone-related UV-B influence on food web processes in the Arctic Ocean is likely to be small relative to the effects caused by variation in the concentrations of natural UV-absorbing compounds, known as chromophoric dissolved organic matter (CDOM), that enter the Arctic basin via its large river inflows. The aim of our present study was to develop and apply a simple bio-optical index that takes into account the combined effects of attenuation by atmospheric ozone and water column CDOM, and photobiological weighting for high-latitude environments such as the Arctic Ocean. To this end, we computed values for a biologically effective UV dose rate parameter ("weighted transparency" or T*) based on underwater UV measurements in high-latitude lakes and rivers that discharge into the Arctic Ocean; measured incident UV radiation at Barrow, Alaska; and published biological weighting curves for UV-induced DNA damage and UV photoinhibition of photosynthesis. The results underscore how strongly the Arctic Ocean is influenced by riverine inputs: shifts in CDOM loading (e.g., through climate change, land-use practices, or changes in ocean circulation) can cause variations in biological UV exposure of much greater magnitude than ozone-related effects.
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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 | 0.000 | 0.000 |
| 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".