Effects of Culverts on Stream Fish Assemblages in the Alberta Foothills
Bibliographic record
Abstract
Abstract Watercourse-crossing structures are ubiquitous anthropogenic features in the Rocky Mountain foothills of Alberta. We performed physical and habitat assessments at 295 watercourse-crossing sites in 15 subbasins of the Athabasca River during the summer and early fall of 2007, 2008, and 2009, sampling for fish at 110 sites (32 bridges and 78 culverts). We used bootstrapping analysis to examine how several culvert parameters (hang height, outlet plunge pool depth, water velocity, length, and slope) altered the upstream abundances of eight fish species relative to those at reference bridge sites. Physical drops at the outlet (hang heights), slope, and outlet water velocities were the most important culvert parameters shaping non-sport-fish distributions. Some culvert types (e.g., hanging culverts) acted as complete barriers to burbot Lota lota and partially impeded the movements of spoonhead sculpin Cottus ricei, suckers Catostomus spp., and minnows (family Cyprinidae). For example, at culverts with high outlet water velocities (>0.59 m/s), the upstream proportion of the total catch for burbot was 0.32 units lower than that at bridge crossings. We did not find evidence that culverts acted as barriers to the upstream passage of rainbow trout Oncorhynchus mykiss; rather, the abundances of rainbow trout significantly increased upstream of the highest-hanging, steepest, and longest culverts. One explanation may be that culverts that exclude burbot, a voracious predator, offer a competitive release for rainbow trout upstream of culverts. However, culverts had significantly higher water temperatures and silt and sand substrates upstream (versus downstream), whereas instream habitat did not differ at bridges. Given the large number of culverts that may be barriers in the Alberta foothills, our research emphasizes the need to better understand how species respond to the characteristics of culverts. Such data are needed to assist with making informed regulatory and planning decisions. Received March 26, 2011; accepted January 19, 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".