Aquatic metabolism throughout impoundment of a low productivity boreal reservoir using free water oxygen
Bibliographic record
Abstract
Inland water bodies play a significant role in the global cycling of greenhouse gases (GHGs). Impoundment of rivers changes their GHG dynamics and leads to a pulse of emissions, primarily due to the respiration of introduced organic matter (OM). Aquatic metabolism estimates using free water oxygen curves were calculated at five sites on the lower Nelson River, Manitoba, Canada to assess the impact of impounding a new hydroelectric reservoir at the Keeyask Generating Station. After impoundment, the greatest ecosystem respiration (R) rates were seen in a former tributary inflow (-1.94 ± 1.03 g C m -2 d -1 ) and then in the forebay (-1.06 ± 0.74 g C m -2 d -1 ), an order of magnitude greater than the upstream extent of the reservoir area that appeared unimpacted (-0.13 ± 0.29 g C m -2 d -1 ). Benthic and pelagic R were of greater relative importance in the former tributary inflow and the forebay, respectively. Loading of allochthonous OM was a key factor regulating R. Further, evidence of "priming," wherein labile OM facilitates the breakdown of more recalcitrant OM, was observed. Light limitation appeared to be prevalent throughout the study area, consistent with previous studies in the region. Despite the unique water chemistry present (i.e., high total phosphorus, high turbidity), aquatic metabolism in the study area appeared similar to other boreal impoundments. The results presented here updated the understanding of aquatic metabolism in a region characterized by hydroelectric development. Further, challenges in the methodology (e.g., gas transfer velocity estimation) were identified and discussed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".