Entrainment of fish eggs and larvae at an operating nuclear generating station using improved methodology
Bibliographic record
Abstract
Patrick PH, Di Giuseppe M, Manolopoulos H, Tai M-K, Poulton S, Wright J. 2020. Entrainment of fish eggs and larvae at an operating nuclear generating station using improved methodology. Lake Reserv Manage. 37:186–198.Entrainment data collected from 7 December 2015 to 22 November 2016 at the Darlington Nuclear Generating Station (DNGS) using automated sampling methodology are discussed in this article, including numbers of fish eggs and larvae entrained. We used a more robust sampling design than previously used in 2004 and 2006, which involved more frequent sampling over a longer, 12 month period. The design allowed collection of daily samples with a longer sampling duration and higher total sample volumes that reduced variability compared to previous studies with less robust sampling throughout a day. Our study resulted in the capture of deepwater sculpin and burbot that were not observed in previous entrainment studies. The maximum entrainment density for eggs was about 17 times higher than that for larvae. The entrainment of fish eggs was highest in the summer months (June and July) and did not vary diurnally. The highest entrainment rates for larvae occurred during the months of August and September, with higher densities entrained at night. We recommend the use of both increased sampling frequency and sampling volumes to characterize fish entrainment for water users in the Great Lakes watershed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".