The impact of a loss of hydrologic connectivity on boreal lake thermal and evaporative regimes
Bibliographic record
Abstract
Abstract This paper summarizes the initial results (first 4 yr) of a whole‐catchment manipulation to evaluate the impact of a loss of hydrologic connectivity of a boreal lake with its watershed on lake thermal and evaporative regimes. We diverted the upstream flow to a fourth order lake resulting in a marked reduction (81%) in watershed area, and compared the response to a comparable control lake in a headwater position. The manipulation reduced runoff into the experimental lake from 2400 mm to 90 mm, greatly increased theoretical residence time (from 2.3 yr to 18.3 yr), and reduced average dissolved organic carbon concentration from 5.8 ± 0.3 mg L−1 to 5.5 ± 0.3 mg L−1. Average Secchi depth was increased by 0.6 m, resulting in a 0.25 m deeper epilimnion and cooler surface water temperatures (8% of the time) than those predicted by pre‐manipulation relationships. There was some evidence that the response of evaporation to episodic events was altered, but this was not extensive enough to alter annual evaporation or median evaporation rates. Our findings show that the impacts of hydrologic connectivity on lake chemistry can cascade to alter the energy budgets of boreal lakes. These results can inform how differences in hydrological connectivity across the landscape due to lake‐watershed topology and climate will impact boreal lakes. Of note is that very clear lakes in the boreal region are highly sensitive to impacts from a change in clarity. Information on their spatial distribution will be necessary to assess impacts of hydrological connectivity on the boreal lake complex.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".