MétaCan
Menu
← Back to cohort
Record W7133065254

Forest fragmentation effects and the cavity nest material requirements of northern flying squirrels and red squirrels in a fragmented secondary hardwood forest region of Ontario, Canada

2008· dissertation· W7133065254 on OpenAlexfundaboutno aff
Jesse Eric-Henry Patterson

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Natural Resources
KeywordsNest (protein structural motif)Fragmentation (computing)HabitatHabitat fragmentationForest fragmentationHardwoodBasal areaEndangered species
DOInot available

Abstract

fetched live from OpenAlex

Agriculturally induced fragmentation of hardwood forests has been extensive in southern Ontario, with important implications for biodiversity, such as the outright loss of species, contraction in species distributions, and reduced genetic diversity. The purpose of this thesis was to investigate the patterns of fragment occupancy, from a metapopulation perspective, as a function of landscape-level features and to identify significant aspects of habitat use, including nest material requirements, for northern flying squirrels (Glaucomys sabrinus) and red squirrels ( Tamiasciurus hudsonicus) in highly fragmented secondary hardwood forests. The occurrence of G. sabrinus was positively correlated with fragment area, while T. hudsonicus occurrence was positively correlated with the basal area of coniferous trees. Both species primarily used shredded eastern white cedar (Thuja occidentalis) bark as nesting material, possibly due to a behavioural adaptation to reduce ectoparasite loads in the nest environment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.182
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueTSpace→Same topicAnimal Ecology and Behavior Studies→French-language works237,207→