Influence of small dams on the stream temperature in a protected area of southern Quebec.
Bibliographic record
Abstract
Small dams represent 99% of the world's water impoundments, but little is known about their effect on river temperature. As stream temperature is an important variable in maintaining the aquatic ecosystems integrity, the study of the effect of small dams is necessary. This study purpose to understand the small dams effet on summer stream temperature in a protected area with low disturbance in southern Quebec, Canada. We compared four attributes of the thermal regime (magnitude, frequency and duration of warm events and rate of change) in streams i) upstream and downstream reservoir regulated by a small dam and ii) downstream reservoirs and natural lakes. with a generalized additive model, we also identified key determinants of August stream temperature. Compared to upstream reservoir conditions, we observed a 3.7°C warming in streams downstream of reservoirs regulated by small dams during August 2020. This warming wasn’t significantly different from that observed between upstream and downstream of natural lakes (3.4 °C). Proximity to an upstream waterbody, drainage area, and proportion of the watershed occupied by waterbodies were the principal determinants of water temperature in August, demonstrating the waterbodies importance on the thermal regime of streams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".