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Record W2788787480 · doi:10.1098/rsbl.2017.0770

Narrow anthropogenic corridors direct the movement of a generalist boreal butterfly

2018· article· en· W2788787480 on OpenAlexafffundabout
Federico Riva, John Acorn, Scott E. Nielsen

Bibliographic record

VenueBiology Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada's Oil Sands Innovation AllianceAlberta InnovatesAlberta Agriculture and ForestryAlberta Conservation Association
KeywordsGeneralist and specialist speciesButterflyBorealEcologyHabitatTaigaArcticEcosystemBiology

Abstract

fetched live from OpenAlex

Ecological and anthropogenic corridors are becoming more common worldwide, but little is known about how corridor size (width) affects species' movements, and thus their effects. Here we investigated whether 4- and 8-m wide anthropogenic corridors (seismic lines) cleared for petroleum (oil sands) exploration in boreal forests in Alberta, Canada, act on altering the behaviour of a habitat generalist butterfly, the Arctic fritillary ( Boloria chariclea ). Specifically, we captured 539 Arctic fritillaries and released them in seismic line corridor or control sites with no structural directionality (i.e. forests and clearings), and recorded both their initial direction (along the seismic line or not) and persistence in directional movements. Arctic fritillaries moved inside these lines twice as often as they left them, and maintained their initial direction more often, regardless of line size and independently of forest structure or sex of individuals. Thus, anthropogenic corridors as narrow as 4 m can affect insect movements. Given the vast area of boreal forests disturbed from seismic assessments, investigating if the effects of these dense, localized lines affect population dynamics and species interactions would provide important insights to managing this ecosystem and identifying restoration actions.

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 score0.116
Threshold uncertainty score0.990

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations37
Published2018
Admission routes3
Has abstractyes

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