Assessing the Magnitude of Effect of Hydroelectric Production on Lake Sturgeon Abundance in Ontario
Bibliographic record
Abstract
Abstract The presence of hydroelectric power generating facilities has been identified as the primary factor affecting the variation in relative abundance of Lake Sturgeon Acipenser fulvescens in rivers across Ontario. Qualitatively, these facilities are known to have impacts on the aquatic environment, and they can be inferred to have effects on Lake Sturgeon; however, few studies quantifying these effects are available. Our objectives were to (1) determine and compare the magnitude of effect (d) of hydroelectric facility operating regimes on Lake Sturgeon abundance; (2) compare Lake Sturgeon biological responses among river systems with different operating regimes in order to understand the potential limiting factors within these systems; and (3) assess the effectiveness of mitigation efforts where they have been employed. A standardized index netting program targeting juveniles and adults was conducted over two field seasons at 23 river sites across Ontario. The magnitude of effect on abundance (as indicated by d) was lowest in run-of-the-river systems and was considered large in peaking systems and winter reservoir systems. Relative abundance was significantly greater in unregulated rivers than in regulated rivers. Juvenile abundance was significantly greater in run-of-the-river systems than in peaking systems and winter reservoirs and was significantly greater in peaking systems than in winter reservoirs. Adult abundance did not significantly differ among operating regimes. Growth was faster and condition was significantly greater in unregulated systems than in regulated systems. Recruitment of Lake Sturgeon was highly variable in both regulated and unregulated systems, whereas recruitment failure was more evident in regulated systems, particularly in peaking systems. Received April 5, 2015; accepted July 2, 2015
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".