Patterns of Distribution and Photooxidation of Fluorescent Dissolved Organic Material in the Arctic Ocean
Bibliographic record
Abstract
Thinner sea ice and increasingly ice-free summers in the Arctic Ocean have the potential to affect the rate of loss of fluorescent dissolved organic material (FDOM) via photooxidation. Photooxidation is the transformation of dissolved organic material to CO2 or lower molecular weight compounds and is a crucial part of the Arctic carbon cycle. Increased sunlight due to thinner ice and more open water could increase the rate of photodegradation, therefore, causing more rapid cycling of dissolved organic material. However, there is little data on this subject due to the challenges of sampling in the Arctic in the early spring. Between 2014 and 2018, six autonomous ice-tethered buoys were deployed in the Chukchi Sea into first-year sea ice. These buoys measured water temperature, light intensity, chlorophyll, and FDOM under the ice starting in the spring. A general pattern of higher concentrations of FDOM on the Chukchi shelf and lower concentrations on the shelf break and the deeper Canada basin were observed in all years. On the Chukchi Shelf, we observed a strong photooxidation trend beginning in July and ending in early September. In comparison, a much slower photooxidation rate was present on the Chukchi Shelf break. In the deep Canada Basin, there was no observed loss due to photooxidation. Higher photooxidation is expected on the Chukchi shelf as the FDOM pool consists of labile compounds from land and river runoff as well as material produced in situ by ice algae and phytoplankton. Material in the Canada Basin is older and often does not contain compounds that respond to photooxidation. With this work we hope to quantify the loss and recycling of DOM in the Arctic Ocean via the photooxidation pathway, leading to predictions for carbon cycling in the future Arctic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".