On the variability of the Priestley‐Taylor coefficient over water bodies
Bibliographic record
Abstract
Abstract Deviations in the Priestley‐Taylor (PT) coefficient αPT from its accepted 1.26 value are analyzed over large lakes, reservoirs, and wetlands where stomatal or soil controls are minimal or absent. The data sets feature wide variations in water body sizes and climatic conditions. Neither surface temperature nor sensible heat flux variations alone, which proved successful in characterizing αPT variations over some crops, explain measured deviations in αPT over water. It is shown that the relative transport efficiency of turbulent heat and water vapor is key to explaining variations in αPT over water surfaces, thereby offering a new perspective over the concept of minimal advection or entrainment introduced by PT. Methods that allow the determination of αPT based on low‐frequency sampling (i.e., 0.1 Hz) are then developed and tested, which are usable with standard meteorological sensors that filter some but not all turbulent fluctuations. Using approximations to the Gram determinant inequality, the relative transport efficiency is derived as a function of the correlation coefficient between temperature and water vapor concentration fluctuations (RTq). The proposed approach reasonably explains the measured deviations from the conventional αPT = 1.26 value even when RTq is determined from air temperature and water vapor concentration time series that are Gaussian‐filtered and subsampled to a cutoff frequency of 0.1 Hz. Because over water bodies, RTq deviations from unity are often associated with advection and/or entrainment, linkages between αPT and RTq offer both a diagnostic approach to assess their significance and a prognostic approach to correct the 1.26 value when using routine meteorological measurements of temperature and humidity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".