Bull trout (<i>Salvelinus confluentus</i>) occurrence and abundance influenced by cumulative industrial developments in a Canadian boreal forest watershed
Bibliographic record
Abstract
We examined relations between cumulative levels of forest harvesting and density of road networks on the occurrence and abundance of bull trout (Salvelinus confluentus) in the Kakwa River Basin, Alberta. Logistic regression models showed that bull trout occurrence was positively related to stream wetted width but negatively related to percent fines, percent cobbles, reach slope, and the cumulative area of the subbasin harvested and road density. Results from zero-inflated Poisson regression models typically showed that bull trout abundance was positively related to elevation and negatively related to stream width, percent fines, percent cobble, slope, and levels of forest harvesting. Using the negative relation between bull trout occurrence and percentage of subbasins harvested derived from the most parsimonious logistic regression model, we forecasted that forest harvesting over the next 20 years is projected to result in the local extirpation of bull trout from 24% to 43% of stream reaches that currently support bull trout in the Kakwa River Basin.
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".