Effectiveness of Spawning Substrate Enhancement for Adfluvial Fish in a Regulated Sub-Arctic River
Bibliographic record
Abstract
This study was conducted to meet regulatory requirements under the Fisheries Act in Canada, specifically for a hydroelectric facility on the Yellowknife River in the Northwest Territories. The research focused on annual snorkel surveys of adfluvial fish and their spawning habitat below the facility. Initial observations of egg mortality, potentially due to overcrowding, prompted the investigation of natural and enhanced habitat for spawning Lake trout ( Salvelinus namaycush ), lake whitefish ( Coregonus clupeaformis ) and cisco ( Coregonus artedi ) from 2016 to 2019. The design and composition of the installed habitat were based on fish utilization of the natural channel below the hydro facility and design principles from previous habitat rehabilitation projects for anadromous fishes. Pre- and post-enhancement data on egg density and survival were collected using 1 m 2 plots on both natural and artificially enhanced substrates. Three years of post-enhancement monitoring indicated higher egg densities and a greater proportion of live eggs in the artificially enhanced habitat compared to the natural habitat, with more pronounced trends observed for coregonids (lake whitefish and cisco) compared to lake trout. These findings suggest that habitat enhancement has the potential to enhance juvenile recruitment for adfluvial fish. A critical factor in the design was the substrate composition, providing adequate interstitial spaces for egg development and protection. This study represents the first documented attempt at habitat improvement in a regulated sub-Arctic river in Canada. The findings offer valuable guidance for stakeholders involved in new or existing development projects that require conservation actions to maintain fisheries productivity.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".