Dissolved organic matter concentration, optical parameters and attenuation of solar radiation in high-latitude lakes across three vegetation zones
Bibliographic record
Abstract
High-latitude lakes are usually transparent, due to their low productivity and low concentration of dissolved organic matter (DOM), but large variations in lake optical properties can be found within and between regions. We investigated the light regimes in relation to DOM in 18 oligotrophic, high-latitude lakes across mountain birch woodland, shrub tundra and barren tundra in north-west Finnish Lapland. In 12 lakes >1% of photosynthetically active radiation (PAR) reached the lake bottom, while 1% UV-B depth ranged from 0.1 to >12 m. Lakes located in barren tundra had highest transparency, lowest dissolved organic carbon (DOC) concentration and lowest DOM absorption (a440) (mean values: Kd PAR 0.3m–1, DOC 2.1mg l–1, a440 0.4m–1), while lakes in shrub tundra and mountain birch forest were less transparent (DOC 4.7 mg l–1, a440 1.4 m–1). Solar attenuation and lake transparency was best explained by a440. Our survey emphasizes the importance of catchment type on DOM characteristics and lake optics. We predict that even small changes in DOM quality may largely change the UV radiation exposure of lakes while changes in PAR may have smaller biological effects in these shallow lakes that are already illuminated to the bottom.
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 | 0.000 |
| 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".