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Record W7079555159 · doi:10.26108/cqs4-9s04

Nest-site characteristics and the influence of clear-cutting at multiple scales on breeding success of migratory songbirds in Newfoundland

2007· article· en· W7079555159 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2007
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatNest (protein structural motif)Vegetation (pathology)Breeding bird surveyBorealTaigaSpatial ecology

Abstract

fetched live from OpenAlex

Animal populations can fluctuate depending on fluctuations at multiple scales in habitat availability and habitat structure. Using nest-monitoring and passive mist-netting, I examined the importance of habitat composition at nest-site (5 m), local (20 m), neighbourhood (115 m), and landscape (1250 m) scales, in relation to breeding success of boreal birds. At the scale of the nest-site, each of three species ('Dendroica coronata, Dendroica striata,' and 'Zonotrichia albicollis') non-randomly situated their nests in terms of structural vegetation variables. However, none of the structural variables that distinguished nest-sites from random sites were important predictors of overall nest success or nest predation. When focusing only on nest-sites, neither of the 'Dendroica' species had structural vegetation variables associated with nest success. However, successful 'Zonotrichia albicollis' nests were associated with less canopy cover and woody debris than unsuccessful nests. At local, neighbourhood, and landscape scales, several trends were observed between habitat composition (proportion of clear-cut) and breeding success. First, habitat composition at broader landscape scales became more important to breeding birds as the breeding period progressed. Second, spatial and temporal heterogeneities interacted to make breeding success difficult to predict. Third, influences of habitat composition differed depending on the species, but forest-nesting specialists were more negatively influenced by increased proportions of clear-cut in the surrounding area.

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)0.001
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 score0.005
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.225
Teacher spread0.216 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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