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Record W7065547018

Effects of Forest Characteristics and Landscape Configuration on Flying Squirrel Occurrence and Abundance in Rouge National Urban Park

2022· other· en· W7065547018 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2022
Typeother
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
FundersParks Canada
KeywordsArboreal locomotionNational parkWildlifeHabitatAbundance (ecology)MammalWildlife conservationWildlife refugeROUGEUrban forest
DOInot available

Abstract

fetched live from OpenAlex

Wildlife conservation is a challenge in urban and peri-urban contexts, as anthropogenic land-use changes result in habitat loss, a reduction in forest patch size, and disconnected and isolated forest patches. As gliding arboreal mammals, flying squirrels are of particular concern in a landscape fragmentation context. Little is known about the distribution or abundance of flying squirrels in southern Ontario. Rouge National Urban Park, the only urban park in Canada, is located in southern Ontario along the Rouge watershed, and has had historical recordings of both southern and northern flying squirrel species. During May-August 2021, 19 sites were selected across the urban and peri-urban environments of Rouge Park, in which data were collected through small mammal trapping and habitat sampling. 7 mammal species were recorded, including both southern and northern flying squirrels. Habitat connectivity was a significant correlate of both flying squirrel species. Patch size was not significant, suggesting that forest fragments across the Rouge are well enough connected to support flying squirrel subpopulations through immigration and inter-patch colonization.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.271
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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