Using Video to Evaluate Depth and Velocity Selection by Arctic Grayling (<i>Thymallus arcticus</i>) in Pools of an Engineered Tundra Stream
Bibliographic record
Abstract
We evaluated pool use by Arctic grayling (Thymallus arcticus) in an engineered stream in the Canadian Barrenlands at the summer background flow (1.0 l/s) and at enhanced flows (9.9 l/s and 21.9 l/s) similar to those during the spring spawning period. We used an acoustic Doppler velocimeter to measure and map out point velocities (horizontal and vertical) in five study pools. The positions of adult Arctic grayling were monitored for each flow condition using visual surveys and a novel video assessment technique. Although fish mobility limited pool selection at the summer background flow, the highest use of pools by fish during enhanced flows occurred where pool designs incorporated scour holes or downstream sills to provide larger amounts of relatively deep water. Within those pools, grayling selected for locations with depths between 0.20 m and 0.30 m and near-zero vertical velocities (−0.02 m/s to 0.04 m/s). Fish selected near-zero horizontal velocities(0.00 m/s to 0.04 m/s) for resting and higher velocities (0.12 m/s to 0.20 m/s) for feeding. In contrast, grayling tended to show local avoidance of areas with horizontal velocities above 0.2 m/s or vertical velocities above 0.04 m/s. Although findings are likely site specific, our study contributes towards the development of size, depth, and velocity criteria for Arctic grayling habitat; this information can promote effective designs for habitat compensation and fish passage projects. We also present a novel video monitoring method that can be easily deployed at remote locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".