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Record W4406202854 · doi:10.1139/cjfr-2024-0247

Seasonal bird communities in Shelterwood harvests and unmanaged mature forest

2025· article· en· W4406202854 on OpenAlexvenueno aff
Cathryn H. Greenberg, Margaret Woodbridge, Maria A. Whitehead, J. Drew Lanham, Charles Kwit, Joseph Tomcho

Bibliographic record

VenueCanadian Journal of Forest Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersSouthern Research Station
KeywordsForestryGeographyEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Effective bird conservation planning requires consideration of year-round habitat requirements. We evaluated how bird communities differed across seasons, and between young shelterwood (SW) and unmanaged mature (M) hardwood forests over 8 years. We detected 3952 individuals of 82 bird species within transects. Total abundance, richness, and diversity were highest in summer and lowest in winter. Richness was greater in SW than M during all seasons; abundance and diversity were greater in SW during summer, fall, and spring. Community composition differed between SW and M during all seasons except winter. Within seasons, abundance of most analyzed species was greater in SW than M or similar between the treatments. In SW young forest habitat suitability for most species (except indigo buntings) persisted for at least 8 years. Residency guilds and some species showed greater habitat selectivity in some seasons (heavier use of SW) than others (similar use of both SW and M). Our study illustrates the important role of young forests in promoting bird diversity year-round. However, knowledge gaps remain regarding bird use of all forest age-classes during all seasons, and how long dynamically changing young forests support more birds and bird species than mature forest during non-breeding seasons.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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