MétaCan
Menu
Back to cohort
Record W2002988598 · doi:10.14430/arctic4386

Shifts in Fox Den Occupancy in the Greater Prudhoe Bay Area, Alaska

2014· article· en· W2002988598 on OpenAlexvenueno aff
Alice A. Stickney, Tim Obritschkewitsch, Robert M. Burgess

Bibliographic record

VenueARCTIC · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsArcticArctic foxBayCircumpolar starGeographyVulpesTundraThe arcticOccupancyEcologyArchaeologyLagopusPredationOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Although shifts in the distribution of red foxes into areas previously dominated by Arctic foxes have been documented over wide areas of the circumpolar North, no such documentation exists yet for the Alaskan Arctic. Fox research in the greater Prudhoe Bay area from the 1970s through the early 1990s focused primarily on Arctic foxes in relation to oil development because red foxes were uncommon. A monitoring program in 2005–12 included annual surveys of 31–48 fox dens within 2 km of the road system. In 2005, 2006, and 2008, Arctic fox dens outnumbered those of red foxes, but from 2010 onward, the reverse was true. There is greater distance between natal dens of Arctic foxes and those of red foxes than between natal dens within each species, suggesting that Arctic foxes avoid red fox denning territories. Of dens in our study that were used by Arctic foxes prior to 2005, 50% have since been occupied by red foxes. Red foxes displaced Arctic foxes from dens closest to oil field camps, pads, and other facilities, and preyed on their pups. Access to anthropogenic food sources probably supports red foxes in the area. Predictions from climate change studies indicate the displacement of Arctic foxes by red foxes will continue in the Alaskan Arctic, although the change may be slower away from areas of human occupation and anthropogenic foods.

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 score0.006
Threshold uncertainty score0.939

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)0.0010.001

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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueARCTICSame topicWildlife Ecology and ConservationFrench-language works237,207