Thermal stratification in small arctic lakes of southwest Greenland affected by water transparency and epilimnetic temperatures
Bibliographic record
Abstract
Abstract We assessed which factors control summer epilimnion thickness in arctic lakes of southwest Greenland. A suite of 22 lakes that thermally stratify was measured in the summer of 2013; a sub‐set of eight of the lakes was measured again in 2014, which was a warmer summer than 2013. Regression analysis of the 22 lakes indicated that the 1% attenuation depth for photosynthetically active radiation (PAR) was the strongest single predictor ( R 2 = 0.75) of epilimnion thickness across lakes; the addition of epilimnion temperature to the PAR model explained additional variability ( R 2 = 0.79). The importance of including temperature in the model was apparent in the results of model validation as well as when comparing across years: while the 1% PAR was 0.4–2 m deeper in 2014 compared with 2013, water temperatures were 2–3°C higher, resulting in July epilimnion thicknesses that were equal to or shallower than in 2013. In these lakes with low color dissolved organic carbon (DOC), multiple factors control the 1% PAR, including absorbance at 440 nm ( a 440 ), 380 nm ( a 380 ), and 320 ( a 320 ), chlorophyll a (Chl a ) and DOC concentration. In 2014, when 1% PAR was deeper than in 2013, a 380, Chl a and DOC were lower in six of the eight lakes. Our results reveal that the thermal structure of these arctic lakes is under complex control by air temperatures and factors that affect PAR attenuation, particularly Chl a and DOC quality, suggesting that continued warming in the Arctic will have strong effects on lake stratification.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".