Effects of flooding and riparian buffers on survival of muskrats (Ondatra zibethicus) across a flashiness gradient
Bibliographic record
Abstract
Increased agricultural production within the Grand Prairie region, USA, has resulted in drainage of most natural wetlands within the landscape. Muskrats ( Ondatra zibethicus (L., 1766)) in this region have shifted much of their distribution to riparian habitats that have unstable flow regimes and flood inundation times that could be related to position within watersheds. We investigated predation risk of radio-marked riparian muskrats during flooding events in relation to landscape position. We used known-fate models and an information–theoretic approach to examine effects of age, season, hydrology, and riparian width on weekly survival rates. During flooding events, muskrats positioned farther from headwaters were displaced for longer, as well as exposed to predation from terrestrial predators for longer, than those positioned closer to headwaters. However, this increased exposure during floods did not translate into lower survival because most mortalities were due to predation by American mink ( Neovison vison (Schreber, 1777)) along stream edges during nonflooding periods. Weekly survival of muskrats was lower in winter (mean = 0.9377, SE = 0.1793) than in nonwinter (mean = 0.9770, SE = 0.0116) and was positively related to riparian width. Larger riparian buffers can increase muskrat survival in small streams and agricultural ditches within highly altered, human-dominated agroecosystems. Our study provides a rare example of linking riparian buffers to fitness for a stream-associated organism.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".