Estimation of Water Temperature of Large Lakes in Cold Climate Regions during the Period of Strong Coupling between Water and Air Temperature Fluctuations
Bibliographic record
Abstract
Abstract Near-surface bulk water temperature measured in large northern lakes during the warm season with buoys can be characterized by three components: a slowly varying seasonal-/climate-related trend, fluctuations at the time scale of weather perturbations, and rapid daily fluctuations. When thermal infrared data are used to estimate surface water temperature, an additional term relating the differences between the skin and bulk temperatures is implied. Buoy data in this work serve to demonstrate the existence of a period of strong signal coupling between fluctuations of water temperature and air temperature. The period of strong signal coupling does not extend beyond the date of maximum temperature in the water temperature trend. During this period, a simple linear transformation of air temperature fluctuations can be used to simulate the buoy water temperature fluctuations. Attempts to simulate water temperature fluctuations from air temperature alone are not possible beyond this period. Water temperature simulation error depends on the distance of the air temperature measurement from the buoy, ranging from ±1.1°C at 0 km to ±1.4°C at 40 km. The method developed with buoy data is applied to the combination of satellite thermal infrared and operationally measured air temperature data to simulate water temperatures. Through the use of satellite data, the water temperature simulations are extended beyond the period of strong coupling.
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 | 0.001 |
| 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 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".