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Record W2886658220 · doi:10.1080/15230430.2018.1487657

Post-breeding movement and habitat use by wood frogs along an Arctic–Subarctic ecotone

2018· article· en· W2886658220 on OpenAlexafffundabout
Stephanie C. Bishir, Blake R. Hossack, LeeAnn Fishback, Jon M. Davenport

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsChurchill Northern Studies Centre
FundersExplorers ClubU.S. Geological SurveyChurchill Northern Studies CentreAmerican Museum of Natural HistoryEarthwatch Institute
KeywordsTundraSubarctic climateEcotoneHabitatEcologyTaigaWetlandArcticBorealOverwinteringClimate changeGeographyBiology

Abstract

fetched live from OpenAlex

By altering essential micro- and macrohabitat conditions for many organisms, climate change is already causing disproportionately greater impacts on Arctic and Subarctic ecosystems. Yet there is a lack of basic information about many species in northern latitudes, including amphibians. We used radio telemetry to study the post-breeding movements and habitat use of wood frogs (Rana [=Lithobates] sylvatica) in the Hudson Bay Lowlands near Churchill, Manitoba, Canada. We tracked fifty-seven frogs (thirty-five males, twenty-two females; mean duration = 16.8 d) from three wetlands during the summers of 2015 and 2016. The three wetlands were representative of the Arctic–Subarctic ecotone, with each wetland surrounded by different proportions of boreal forest and tundra. Our results indicate that at the landscape scale, movement distances increased with temperature, and all frogs spent more time in the tundra habitat than in boreal forests, relative to the availability of each habitat type. At the microhabitat scale (1 m2 plots), frogs selected areas with greater amounts of standing water, sedge, and shrubs. These results provide information on terrestrial movement patterns and critical habitat data for northern populations of wood frogs in a Subarctic environment, which will aid in understanding how climate change will affect amphibians in this rapidly changing ecosystem.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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 head0.054
GPT teacher head0.317
Teacher spread0.262 · 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 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

Citations11
Published2018
Admission routes3
Has abstractyes

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