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Record W6929552458 · doi:10.5061/dryad.866t1g1wp

Marsh interspersion and muskrat (Ondatra zibethicus) habitat use

2025· dataset· en· W6929552458 on OpenAlexaffabout

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsTrent University
Fundersnot available
KeywordsMarshHabitatWetlandPopulationWaterfowl

Abstract

fetched live from OpenAlex

Muskrat (Ondatra zibethicus) populations have been declining in North America for decades. The precise cause of these widespread declines has not yet been identified. Over a similar timeframe, wetlands across large regions of North America have been experiencing an invasion of hybrid cattail *Typha *x glauca. This invasion is associated with many negative consequences for wetlands, including a reduction in biodiversity, open water habitat, and interspersion of water and vegetation. Muskrats are strongly tied to wetlands, especially where there is a high degree of interspersion of water and emergent vegetation. Therefore, a widespread reduction in interspersion caused by *T. *x *glauca *invasions may be contributing to widespread muskrat population declines. We sought to understand the impact of reduced marsh interspersion on fine-scale muskrat habitat use which will shed more light on broad-scale population trends. We measured intensity of habitat use by muskrats in a large, Typha-dominated marsh in south-central Ontario using camera traps, stratifying camera placement along a gradient of marsh interspersion. We found no correlation between interspersion and intensity of use, suggesting that factors other than interspersion may drive intensity of use. The best predictor of intensity of use in our study was the presence of channelized water features. Our study site, like most marshes in the region, was highly dominated by *T. *x glauca. Further research is needed to determine the impact of *T. *x *glauca *invasions on muskrats, as well as the cause of widespread muskrat declines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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.

Opus teacher head0.018
GPT teacher head0.282
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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