Rainbow trout (<i>Oncorhynchus mykiss</i>) habitat overlap with wild Atlantic salmon (<i>Salmo salar</i>) and brook trout (<i>Salvelinus fontinalis</i>) in natural streams: do habitat and landscape factors override competitive interactions?
Bibliographic record
Abstract
The purpose of this study was to update rainbow trout (Oncorhynchus mykiss) invasion status, delineate factors that increase the invasion probability, and quantify habitat overlap between invasive rainbow trout and native Atlantic salmon (Salmo salar) and brook trout (Salvelinus fontinalis) on Prince Edward Island, Canada. Analysis of landscape-level variables in 26 watersheds (14 with and 12 without rainbow trout) demonstrated that watershed slope, percent agricultural land use, and distance to the nearest rainbow trout population significantly increased the probability of rainbow trout establishment. Two independent reach-level fish community surveys were conducted by electrofishing in combination with habitat surveys. Reaches with rainbow trout had higher stream slope, Atlantic salmon were found in wider reaches with larger substrate, and brook trout were typically dominant in headwater reaches with finer substrate. Findings at multiple ecological scales illustrated that rainbow trout invasion success is positively influenced by the presence of high-slope habitat. Habitat separation of the three salmonid species indicates that competition with introduced rainbow trout may not be the most significant threat to native salmonid populations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".