Similarity scaling of turbulence in a temperate lake during fall cooling
Bibliographic record
Abstract
Abstract Turbulence, quantified as the rate of dissipation of turbulent kinetic energy (ε), was measured with 1400 temperature‐gradient microstructure profiles obtained concurrently with time series measurements of temperature and current profiles, meteorology, and lake‐atmosphere fluxes using eddy covariance in a 4 km2 temperate lake during fall cooling. Winds varied from near calm to 5 m s−1 but reached 10 m s−1 during three storm events. Near‐surface values of ε were typically on the order of 10−8 to 10−7 m2 s−3 and reached 10−5 m2 s−3 during windy periods. Above a depth equal to |LMO|, the Monin‐Obukhov length scale, turbulence was dominated by wind shear and dissipation followed neutral law of the wall scaling augmented by buoyancy flux during cooling. During cooling, εz = 0.56 /kz + 0.77 JB0 and during heating εz = 0.6 /kz, where is the water friction velocity computed from wind shear stress, k is von Karman's constant, z is depth, and JB0 is surface buoyancy flux. Below a depth equal to |LMO| during cooling, dissipation was uniform with depth and controlled by buoyancy flux. Departures from similarity scaling enabled identification of additional processes that moderate near‐surface turbulence including mixed layer deepening at the onset of cooling, high‐frequency internal waves when the diurnal thermocline was adjacent to the air‐water interface, and horizontal advection caused by differential cooling. The similarity scaling enables prediction of near‐surface ε as required for estimating the gas transfer coefficient using the surface renewal model and for understanding controls on scalar transport.
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".