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Record W2606522224 · doi:10.1139/as-2016-0025

Vulnerability to geomorphological hazards of an Arctic cliff-nesting raptor, the rough-legged hawk

2017· article· en· W2606522224 on OpenAlexafffundvenueabout
Andréanne Beardsell, Gilles Gauthier, Daniel Fortier, Jean‐François Therrien, Joël Bêty

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité du Québec à RimouskiUniversité de MontréalUniversité LavalCenter for Northern Studies
FundersNatural Resources Canada
KeywordsLagopusCliffNest (protein structural motif)ButeoEcologyArcticClimate changeGeographyArctic foxPhysical geographyEnvironmental sciencePredationBiologyArchaeology

Abstract

fetched live from OpenAlex

Increase in temperature and precipitation associated with climate change may enhance the risk of destruction by geomorphological processes of nests or dens used by Arctic wildlife. We assessed nest vulnerability to mass movements and identified environmental factors associated with the persistence of nesting structures of rough-legged hawks (Buteo lagopus), a species that typically nests on steep slopes or cliffs. The study was conducted on Bylot Island (Nunavut) where 82 permanent hawk nesting structures, built mainly on sedimentary rocks, were monitored from 2007 to 2015. More than a quarter of known nests were destroyed during the course of the study and among those still intact, more than half were associated with a moderate to high risk of being destroyed. Nest survival analysis suggested a relatively short persistence of rough-legged hawk nesting structures on Bylot Island compared to other Arctic cliff-nesting species. Nest destruction probability increased for nests built on unconsolidated sediments, with heavy rainfall and temperature during the summer. The anticipated increase in precipitation and temperature due to climate change is likely to augment the exposure of hawk nests to mass movements, which could ultimately reduce the availability of suitable sites for the reproduction of this Arctic-nesting raptor.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.304
Teacher spread0.247 · 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

Citations8
Published2017
Admission routes4
Has abstractyes

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