Influences of Human Stressors on Fish-Based Metrics for Assessing River Condition in Central Alberta
Bibliographic record
Abstract
Abstract Economic developments in Alberta have resulted in widespread changes in land use that may deteriorate river conditions for fish. Fish assemblages were characterized with index of biological integrity metrics for the heavily-developed watershed of the Battle River, Alberta. Metric relationships with human stressors were quantified using regression and information theory methods. Although the fauna comprised 14 native species, 50% of the catch was white sucker (Catostomus commersoni Lacepede, 1803). Five statistically unrelated metrics were identified as being responsive to stressors: two trophic guilds, one habitat guild, one reproductive guild, and one measure of community structure. Regression showed that the cumulative effect of human developments, indexed as road density in the basin, was negatively linked to the relative abundance of lithophils and positively linked to the relative abundance of omnivores. Agriculture also threatened the integrity of fish assemblages. Stream sections with higher cattle densities in their basins had fewer lithophils and benthic invertivores; whereas stream sections with higher nutrient concentrations contained fewer species, as well as fewer top carnivores, but more true omnivores. Understanding effects of human footprints that are expanding in western Canada will be critical to the successful management of aquatic resources.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".