Limitations of Non‐Volitional Upstream Passage for Alewife ( <scp> <i>Alosa pseudoharengus</i> </scp> ) and Blueback Herring ( <scp> <i>Alosa aestivalis</i> </scp> )
Bibliographic record
Abstract
ABSTRACT We used PIT telemetry ( n = 10,292 fish tagged) to evaluate upstream passage at a non‐volitional fishway (trap, lift, and truck) that passed approximately 10‐million river herring (alewife Alosa pseudoharengus and blueback herring A . aestivalis ) around the lowermost dam in the Wolastoq/Saint John River, NB between 2020 and 2023. Between 26% and 62% of tagged fish reached the fishway crowding pool, while less than 14% were detected passing upstream. River herring experienced considerable passage delays (median = 3 days) after reaching the crowder entrance. The probability of passing on the date of first detection was only 10%, and it was positively correlated with the rate of fishway operation (i.e., fish lifts/unit time). The rate and probability of passage were greater for alewife than blueback herring and increased with total length for both species. Collectively, our results suggest that passage efficiency and duration were limited by the movement capacity and operation frequency of the fishway, and potentially the (high) number of fish attempting to pass at a given time. Ultimately, if the design and operation of non‐volitional fishways do not accommodate the size and behavior (i.e., schooling density and migration time) of target populations, our results indicate that potential consequences may include passage delays, reductions in passage efficiency, and selective pressures (e.g., size and species) on target populations.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".