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Record W2593467588 · doi:10.1002/jwmg.21210

Shallow gas development and grassland songbirds: The importance of perches

2017· article· en· W2593467588 on OpenAlexafffundabout
Jennifer Rodgers, Nicola Koper

Bibliographic record

VenueJournal of Wildlife Management · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCenovus Energy
KeywordsGrasslandVegetation (pathology)Threatened speciesWildlifeHabitatEnvironmental scienceAbundance (ecology)EcologyGeographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Grassland bird species have declined more than birds of any other region in North America and industrial development may exert further pressure on these species. We evaluated effects of conventional natural gas infrastructure on the relative abundances of grassland songbirds in southeastern Alberta, Canada at sites with shallow gas well pad densities ranging from 0 to 16 pads/258 ha (0–24 well heads/258 ha). Conventional gas wells have a relatively small footprint and minimal associated noise and maintenance activities, allowing us to focus on effects of the infrastructure itself and vegetation surrounding wells. We conducted fixed‐radius point counts and vegetation sampling at 34 sites in 2010 and 40 sites in 2011. We used generalized linear mixed models and information theory to evaluate effects of infrastructure on birds. Relative abundances of vesper sparrow ( Pooecetes gramineus ) and western meadowlark ( Sturnella neglecta ) increased near gas wells, whereas abundance of the threatened Sprague's pipit ( Anthus spragueii ) declined. Vegetation near infrastructure was shorter and sparser than locations farther from wells, but discrepancies with avian habitat preferences suggest that, in contrast to conclusions of previous studies, vegetation structure could not explain responses to infrastructure by birds. Instead, gas wells may have acted as artificial shrubs because they attracted species that use vegetation for perching but were avoided by species that avoid shrubs. Our results suggest that observed effects were a direct result of the presence of wells and associated fencing, and thus risk mitigation should focus on reducing the extent of aboveground infrastructure. © 2017 The Wildlife Society.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.295

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.233
Teacher spread0.218 · 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

Citations26
Published2017
Admission routes3
Has abstractyes

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