Temporal dynamics of dissolved organic matter (DOM) in mountain lakes: the role of catchment characteristics
Bibliographic record
Abstract
Climate change is affecting mountain ecosystems by increasing vegetation coverage and altering meteorological conditions. These changes are likely to impact the timing and magnitude of dissolved organic matter (DOM) inputs to lakes from the surrounding catchment. We examined temporal dynamics of DOM using in situ optical sensors that measured DOM fluorescence (fDOM) through the ice-free season in five lakes with differing catchment characteristics. We also measured changes in lake level and compiled daily meteorological data from nearby weather stations. At a seasonal time scale, fDOM dynamics occurred in two phases. fDOM declined in the first phase, which lasted until late July – mid-August, and corresponded to a decline in lake level following spring snowmelt. This decline was more pronounced in lakes with more vegetated catchments. At a shorter time scale, fDOM increased following precipitation events with a 0- to 1-day lag. Rates of fDOM increase per centmetre change in lake level were greater in lakes with vegetated catchments. As climate change increases vegetation coverage, DOM will likely become more dynamic at daily and seasonal time scales and impact water transparency and productivity of mountain lakes.
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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 | 0.001 |
| 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 | 0.001 | 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".