Growing season evapotranspiration in boreal fens in the Athabasca Oil Sands Region: Variability and environmental controls
Bibliographic record
Abstract
Abstract Current efforts to assess changes to the wetland hydrology caused by growing anthropogenic pressures in the Athabasca Oil Sands Region (AOSR) require well‐founded spatial and temporal estimates of actual evapotranspiration (ET), which is the dominant component of the water budget in this region. This study assessed growing season (May–September) and peak growing season (July) ET variability at a treed moderate‐rich fen and treed poor fen (in 2013–2018), open poor fen (in 2011–2014), and saline fen (in 2015–2018) using eddy covariance technique and a set of complementary environmental data. Seasonal fluctuations in ET were positively related to net radiation, air temperature and vapour pressure deficit and followed trends typical for the Boreal Plains (BP) and AOSR with highest rates in June–July. However, no strong effect of water table position on ET was found. Strong surface control on ET is evident from lower ET values than potential evapotranspiration (PET); the lowest ET/PET was observed at saline fen, followed by open fen, moderately treed fen, and heavily treed fen, suggesting a strong influence of vegetation on water loss. In most years PET exceeded precipitation (P), and positive relations between P/PET and ET were observed with the highest July ET rates occurring under P/PET ~1. However, during months with P/PET > 1, increased P/PET was associated with decreased July ET. With respect to 30‐year mean values of air temperature and P in the area, both dry and wet, cool and warm growing seasons (GS) were observed. No clear trends between ET values and GS wetness/coldness were found, but all wet GS were characterized by peak growing seasons with high daily ET variability.
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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 | 0.001 | 0.001 |
| 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".