Evaluating Remote Site Incubators in Michigan Streams: Implications for Arctic Grayling Reintroduction
Bibliographic record
Abstract
Abstract The successful use of remote site incubators (RSIs) to rear eggs of Arctic Grayling Thymallus arcticus along Montana streams has sparked interest in reestablishing the species in Michigan. As a preparatory step, we assessed the efficacy of RSIs by deploying them along three Michigan streams during 2 years using surrogate eggs from Rainbow Trout Oncorhynchus mykiss. Our objectives were to (1) compare hatching success between two different RSI designs (19-L versus 265-L RSIs), (2) test whether the removal of dead eggs (“picking”) from 19-L RSIs affected hatching success, and (3) develop a simple model to predict fry yield and its uncertainty. Overall survival was 41.3% in 2018 and 52.4% in 2019. Differences in survival between unpicked 19-L and 265-L RSIs tended to be small, with mean differences from 4.82% (95% CI = –0.60 to +10.25) in 2018 to 0.08% (95% CI = –0.14 to +0.30) in 2019. On average, picked 19-L RSIs had greater, although not always statistically significant, survival than unpicked 19-L RSIs during both years (mean difference = 1.6% [2018] and 10.4% [2019]). We documented a significant positive correlation between survival and RSI flow rate. Survival abruptly declined in unpicked 19-L RSIs when RSI flow rates dropped below ~0.3 L/min, suggesting that removing dead eggs from 19-L RSIs likely increased survival when RSI flow rates were <0.3 L/min. The most notable result from our fry yield model was that increasing the number of RSIs reduced the coefficient of variation in fry yield following a pattern of diminishing returns, suggesting two or three RSIs usually will be a good choice. We showed that 19-L and 265-L RSIs can be used successfully in Michigan streams, with our model providing a tool for managers to explore the relative importance of several properties of RSI design and operation on fry yield and uncertainty.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".