Evaporation from land surface in high latitude areas: a review of methods and study results
Bibliographic record
Abstract
Evaporation (ET) from land surfaces in high latitudes is examined on a circumpolar perspective based upon the study results obtained in various environments, from boreal forest (taiga) to the high Arctic desert. Direct and indirect methods of evaporation measurement are reviewed, as well as numerous computational techniques. We have focused upon methods conveniently adopted for calculating evaporation when detailed information on meteorological conditions within the surface boundary layer is not available. These methods range from complicated ones, such as eddy correlation, energy balance and Penman equations, to empirical relationships between ET and incoming solar radiation. Great attention was paid to the principles of each method, especially those developed in Russia as they differ from most of the methods utilized internationally. For example, the Budyko–Zubenok empirical scheme is based upon the principle of potential evaporation, which is affected by soil moisture (SM). This relationship between ET and SM, expressed in terms of the field capacity, has been found to be non-linear; a complication that is not typically accounted for in traditional approaches. This paper also contains a brief review of a number of evaporation case studies including Alaska (USA), north-western Russia and Siberian taiga, Yukon basin (Canada), mountainous forest on Hokkaido Island (Japan), Canadian Arctic and glacierized basins of Greenland.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".