A comparison of metrics to evaluate the effects of hydro-facility passage stressors on fish
Bibliographic record
Abstract
Hydropower is the most common form of renewable energy, and countries worldwide are considering expanding hydropower to new areas. One of the challenges of hydropower deployment is mitigation of the environmental impacts including water quality, habitat alterations, and ecosystem connectivity. For fish species that inhabit river systems with hydropower facilities, passage through the facility to access spawning and rearing habitats can be particularly challenging. Fish moving downstream through a hydro facility can be exposed to a number of stressors (e.g., rapid decompression, cavitation, blade strike and collision, turbulence, and shear forces), which can affect fish survival in direct and indirect ways. Many studies have investigated the effects of hydroturbine passage on fish; however, the comparability among studies is limited by variation in the metrics and biological endpoints used. Future studies investigating the effects of hydroturbine passage should focus on using metrics and endpoints that are easily comparable. This review summarizes four categories of metrics that are used in fisheries research that have application to hydroturbine passage (i.e., mortality, injury, physiological markers, behavior) and evaluates them based on several criteria (i.e., resources needed, invasiveness, comparability among stressors and species, and diagnostic properties). Additionally, these comparisons are put into context of study setting (i.e., laboratory versus field). Overall, injury and physiological markers are ideal for studies in which there is a need to understand the mechanisms of effect, whereas behavior and mortality metrics provide information on the whole body response of the fish. The study setting strongly influences the comparability among studies. In laboratory-based studies, stressors can be controlled by type, intensity, and duration, allowing for easy comparisons among studies. In contrast, field studies expose fish to realistic passage environments but the comparability is limited. Based on these results, future studies, whether lab or field-based, should focus on metrics that relate to mortality for ease of comparison, while some diagnostic capability is important for furthering the science.
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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 | 0.000 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".