Effects of Simulated Blasting on Mortality of Rainbow Trout Eggs
Bibliographic record
Abstract
Abstract Blasting in or near water can negatively affect fish. In Canada, there are maximum allowable limits for blasting‐induced overpressure (100 kPa) and peak particle velocity (PPV; 13 mm/s) to protect fish and their incubating eggs, respectively. No studies, however, have related PPVs from blasting to egg mortality. To address this information gap, we developed a laboratory blast simulation procedure for relating egg mortality to different levels of PPV exposure. Eggs of rainbow trout Oncorhynchus mykiss were subjected to PPVs of up to 245.4 mm/s during six sensitive developmental stages. Eggs also were exposed to a previously described drop height method, in which the final velocity of the eggs is used to estimate PPV exposure; we tested both the original out‐of‐water treatment and an in‐water drop height treatment. Using blast simulation, egg mortality increased at only one developmental stage and only from exposures greater than 132.3 mm/s. Mortality was greater when eggs were placed in spawning gravel versus free in containers, although mortality generally increased at the same PPV level for both treatments. In the drop height method, eggs held out of water were more sensitive to a given exposure level than were eggs held in water. The drop height method may not provide an accurate assessment of blasting‐induced PPVs, especially when eggs are out of water, but should be suitable for comparing the egg sensitivity of different species or development stages. Our controlled, laboratory‐based results indicate that the Canadian PPV guidelines provide ample protection for rainbow trout eggs within spawning beds.
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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 | 0.000 | 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".