Understanding the peak growing season ecosystem water‐use efficiency at four boreal fens in the Athabasca oil sands region
Bibliographic record
Abstract
Abstract Ecosystem water‐use efficiency (WUE), a ratio between gross ecosystem production (GEP) and water loss through evapotranspiration (ET) can be helpful for the assessment of coupled peatland carbon and water cycles under anthropogenic changes in the Athabasca oil sands region (AOSR) where extensive oil sands development has been occurring since the 1960's. As such, this study assessed multiyear peak growing season variability of WUE at four fens (poor treed, poor open, treed moderate‐rich, open saline) near Fort McMurray using the eddy covariance technique combined with a set of environmental variables. Freshwater fens were characterized by WUE values within the range reported from other boreal wetlands while a saline fen had significantly lower values of WUE. Negative correlation (Rs < −0.55, p < 0.05) between WUE and net radiation was observed. Moisture conditions were responsible for interannual differences in WUE, whereby increasing WUE under wetter conditions was observed. However, such a pattern was offset by decreased air temperature (Tair) resulting in moisture oversupply. This study also revealed a negative effect of wildfire on WUE due to a prominent decline in GEP and a moderate decrease in ET. WUE can be useful for monitoring the functioning of natural and constructed fens, but a better understanding of WUE variability under a wide range of climatic conditions with respect to differences in vegetation is required.
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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 | 0.000 |
| 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".