Climate‐related variations in mixing dynamics in an Alaskan arctic lake
Bibliographic record
Abstract
Mean epilimnetic temperatures from mid‐June through mid‐August in a small, arctic kettle lake had no trend from 1975 to 2008 and varied annually up to ±3°C relative to the mean. Analysis of data from temperature arrays deployed on the lake from 1998 to 2007 showed that as mean summer temperatures shifted from 2.5°C below the mean, to the mean, and to 3°C above the mean, deepening of the mixed layer during cold fronts decreased, average metalimnetic thickness increased from 2 to 5 m, maximum values of water‐column stability increased fourfold, minimum values of Lake numbers (LN) increased from ≤1 to 10, the metalimnetic coefficient of eddy diffusivity (Kz) decreased from 10−5 m2 s−1 to 10−7 m2 s−1, and time scales for mixing across the metalimnion increased from days to months. Mean surface temperatures and mixing regimes were significantly correlated with mean air temperatures, but not with mean insolation, or mean wind speeds during summer. They also depended upon the frequency and persistence of events with higher winds, heating, or cooling. In summers with cold surface temperatures, the surface energy fluxes that induce mixing by heat loss were low but with frequent wind events heat was mixed downwards, leading to lower stability. The warmest surface temperatures resulted when atmospheric conditions led to persistent positive buoyancy flux in early summer and winds were elevated primarily on diel cycles as opposed to longer ones. Summers with cooler water temperatures and enhanced vertical mixing are linked to frontal activity and low atmospheric pressure near the northern Alaskan coast.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".