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Record W7057420190

Investigating factors that set the lower elevational limit of Canada jays (Perisoreus canadensis) on Vancouver Island

2021· dissertation· en· W7057420190 on OpenAlexfundaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbiotic componentPasserineCacheRange (aeronautics)Biotic componentHoarding (animal behavior)Climate change
DOInot available

Abstract

fetched live from OpenAlex

Although evidence suggests that climate change will have a major effect on the distribution of species, the abiotic and biotic factors responsible for determining the present range of most species are unknown. The Canada jay (Perisoreus canadensis) is a resident passerine that is limited at the southern edge of its range by climate, via its effect on cached food. To examine the influence of the antimicrobial properties of tree species and temperature on cached food, we conducted two simulated caching experiments. Food cached on Yellow cedar trees (Cupressus nootkatensis) and Amabilis fir (Abies amabilis), retained greater mass than other tree species. We also found evidence that elevation, a proxy for temperature, influences cache preservation. Our results suggest that biotic and abiotic factors can impact the quality of cached food, but we were unable to provide definitive evidence that either was responsible for setting the lower elevational limit of Canada jays.

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)0.001
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 score0.309
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.214
Teacher spread0.198 · 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
Published2021
Admission routes2
Has abstractyes

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