Evapotranspirative Controls in a Low Arctic Tundra Environment, Daring Lake, NWT, Canada
Bibliographic record
Abstract
Determining the extent to which changes in vegetation assemblages influence evapotranspiration in the Arctic could potentially contribute to a more realistic estimation of evaporation in a warming climate.\nThis project aims to determine whether variations in PET and AET rates measured at six tundra vegetation communities can be attributed to the differing vegetation. This will provide a more realistic estimate of change in the water and energy cycles, as well as evaporative processes for a warmer future, caused by enhanced global warming. Predictions of temperature and precipitation regarding future climate in Canada’s Western low Arctic vary greatly. The majority of existing Global Climate Models, regardless of how predicted precipitation increases, indicate that the moisture deficit in the Canadian arctic will grow, due to an increase in evaporation.\nWeighted mean AET was estimated for the year 2040 using four scenarios detailing differing changes in summer air temperature and soil moisture. Given a new distribution of plant communities, it was found that any differences in mean temperature produced negligible effects on forecast ET, whereas an increasing soil moisture deficit lead to lower ET.\nEvapotranspiration was estimated using field data obtained at Daring Lake, NWT, between June 21 and August 18, 2006. Potential evapotranspiration (PET) was quantified using the Priestly-Taylor method. Results ranged varied between 2.2 and 5.6 mm/day and varied between sites. Actual evapotranspiration (AET) was quantified using a series of lysimeters in five different vegetation communities. Lysimeter results ranged between 1.3 and 3.2 mm/day. Using and ICONOS imaging map of the Daring Lake region (Figure 3.6), coverage was estimated each sampled vegetation community and a weighted mean AET for the Daring Lake Study Site was calculated: 2.2 mm/day.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".