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Record W2028300289 · doi:10.1139/x06-010

Songbird diversity and movement in upland and riparian habitats in the boreal mixedwood forest of northeastern Ontario

2006· article· en· W2028300289 on OpenAlexvenueaboutno aff
Erin E. Mosley, Stephen B. Holmes, Erica Nol

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneSpecies richnessEcologyHabitatGeographyBorealRiparian forestAbundance (ecology)TaigaBiodiversityUnderstoryEnvironmental scienceBiologyCanopy

Abstract

fetched live from OpenAlex

Little is known about the importance of riparian areas in supporting avifaunal diversity in the boreal mixedwood forest, especially outside of the breeding season. Bird populations were sampled by mist netting 18 upland and 18 riparian sites along six streams in a forested region of northeastern Ontario. Riparian sites generally had more variable vegetation than upland sites. Some riparian sites formed distinctive habitats, while others were structurally and compositionally similar to upland sites. During spring and fall migration, there was no significant difference in bird abundance or species richness between riparian and upland habitats. During the breeding period, riparian areas had greater avian species richness and abundance and more insects than upland forests, suggesting that birds were selecting these habitats because they contain more food. More birds were captured in nets placed perpendicular to the stream than parallel during the breeding and fall migration periods, suggesting that riparian areas may function as movement corridors. A greater understanding of the importance of riparian habitats to songbird communities is needed if we are to maximize the effectiveness of these regions for conserving avian biodiversity in the boreal mixedwood forest.

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 score0.210
Threshold uncertainty score0.212

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 science0.0000.000
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 head0.023
GPT teacher head0.236
Teacher spread0.212 · 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

Citations17
Published2006
Admission routes2
Has abstractyes

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