Body size, experience, and sex do matter: Multiyear study shows improved passage rates for alewife (<scp><i>Alosa pseudoharengus</i></scp>) through small‐scale <scp>D</scp>enil and pool‐and‐weir fishways
Bibliographic record
Abstract
Abstract Alewife ( Alosa pseudoharengus ) passage through 3 fishways was assessed during the 2013–2016 spawning runs in 3 rivers of the Isthmus of Chignecto, Canada. From April 24 to June 10, 5,423 alewife with a mean ± SD fork length of 227 ± 18 mm were tagged with passive integrated transponders. During their tagging year, approximately half of individuals (40% to 64%) went undetected whereas those detected used fishways from April 16 to July 8. Detected alewife were significantly longer than those undetected. Attraction rates to fishway entrances in 2015 and 2016 ranged from 85% to 98%. Annual fishway passage rates for pooled fish tagged that year and returnees, varied from 64% to 97% for 2 Denil style fishways. A pool‐and‐weir fishway that was dysfunctional (2013), repaired (2014), and replaced (2015–2016) yielded 0.5%, 25%, 60%, and 73% annual pooled passage rates, respectively. Larger individuals, previously tagged returnees, and males compared to females of a similar size had higher passage success suggesting some fishways may apply population‐level selective pressures. Alewife passage rates related to fishway style, design, and proper function, with greater passage for the 2 Denils than the pool‐and‐weir fishway in our study. Regular structural maintenance and fish passage reviews are essential management considerations to ensure fishway functioning and river connectivity. Replacement of a fishway with poor fish passage may be the best option to improve passage rates. Future research should address the effects of multiple anthropogenic instream obstructions, environmental variables, negative sublethal post‐tagging effects, and the importance of returnees on fish passage rates in fishways.
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 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".