Multi‐Species Fish Habitat Preferences for Various Modified Concrete Armouring Designs to Enhance Shoreline Biodiversity
Bibliographic record
Abstract
ABSTRACT Human actions, such as the construction of concrete retaining walls as a form of shoreline armouring, pose an increasing threat to freshwater ecosystems. Conventional concrete armouring methods frequently result in habitat homogenization, which has a detrimental effect on aquatic biodiversity. This laboratory study examined the habitat preferences of four fish species (Yellow Perch [Perca flavescens], Bluegill [Lepomis macrochirus], Banded Killifish [Fundulus diaphanus] and Rock Bass[Ambloplites rupestris]) experimentally introduced to three types of concrete armouring treatment panels with different surface relief depths (5.08 cm, 7.62 cm and 10.16 cm) intended to create structural complexity paired with a flat wall control panel in 20 min dichotomous choice behavioural assays. We found that both species and treatment had a significant impact on space use, with the proportion of time spent near the different treatment panels varying among species. Compared to the treatment panels, fish spent less time near the flat control panels on average, indicating that the treatments' increased structural complexity provided more desirable habitat. Bluegill spent more time near the treatment panels than Banded Killifish and Yellow Perch, while Rock Bass spent more time near the treatment panels than Banded Killifish. As such, future efforts to implement such armouring in the field should consider using panels with a diversity of reliefs to ensure that these structures provide benefit to a wide range of fishes. Our findings highlight the possibility of using novel concrete armouring designs as alternatives to flat retaining walls to improve habitat complexity and benefit freshwater biodiversity where armouring is required.
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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) | 0.002 | 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".