Arctic Grayling Movements through a Nature‐Like Fishpass in Northern Canada
Bibliographic record
Abstract
Abstract Using a PIT detection system and two in‐stream, swim‐through antennas, we examined the movements of Arctic Grayling Thymallus arcticus through a low‐gradient (<1%), nature‐like fishpass that connected two small lakes in the Barrenlands region of northern Canada. We used an ensemble of generalized linear mixed models to evaluate whether passage events (1) were related to fish FL, water depth in the fishpass, and/or temperature in the fishpass; and (2) exhibited any diel patterns. During two seasons, passage events were not related to fish FL or fishpass water temperature; however, the probability of a passage event occurring increased with increases in fishpass depth, which likely served as a proxy for velocity and/or discharge. Most notably, 95% (n = 193/204) of Arctic Grayling passages occurred at night (1800–0559 hours) throughout our study. Although the cause(s) of this diel pattern were not examined directly, we hypothesized that it represented a response to avian predation given the shallow depth of the fishpass and given our observations of daytime avian predation events on Arctic Grayling in the littoral zones of the study lakes. Our results offer novel insights into correlates of Arctic Grayling passage through a fishpass and lay the foundation for future studies to address the hypotheses supported herein with well‐designed experiments to determine the mechanisms behind the patterns we observed. Received September 8, 2015; accepted April 7, 2016 Published online July 28, 2016
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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 | 0.001 | 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".