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Record W2165155421 · doi:10.22230/jem.2001v1n1a212

Observations on the use of stubs by wild birds: A 10-year update

2001· article· en· W2165155421 on OpenAlexaff
Brian D. Harris

Bibliographic record

VenueJournal of Ecosystems and Management · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsKamloops Art GalleryPenticton Regional Hospital
Fundersnot available
KeywordsWildlifeSnagHabitatNesting (process)LoggingHectareNest (protein structural motif)EcologyGeographyForestryAgroforestryEnvironmental scienceBiologyEngineeringAgriculture

Abstract

fetched live from OpenAlex

In British Columbia, many species of wildlife depend on dead or dying trees; however, current Workers� Compensation Board regulations require that such trees be felled. In 1990, in an effort to reconcile workers� safety with wildlife habitat needs, Pope and Talbot Limited proposed the creation of a number of tall stumps (3–5 m tall) in their logging operations. In the study cutblock, approximately 170 lodgepole pine stumps (“stubs”) were cut. Since their establishment, the stubs were monitored for bird nesting each spring. A total of 86 active nests have been counted in 10 years. Ninety-five percent of this nesting occurred in stubs in the clearcut portion of the block, versus 5% in the selectively logged portion. Approximately 16% of the stubs were used for nesting at least once during the 10 years of observations. In general, the greater the diameter of the stub, the greater likelihood that it would be used for nesting. All nesting occurred in reworked holes; no new nest holes were drilled in these stubs. Stub creation should continue to be a part of the wildlife tree management strategy in any logging operation, irrespective of the species of tree being harvested. The average density should be at least one stub per hectare, but preferably much higher to ensure that suitable nest stubs are retained. Stubs that are not used for nesting may provide perching or feeding sites, and contribute to the area�s coarse woody debris when they fall. Stub creation involves little extra cost and little volume is lost. Therefore, all forest companies should be encouraged to create stubs as part of responsible forest stewardship.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.248
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.006
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.029
GPT teacher head0.211
Teacher spread0.182 · 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

Citations3
Published2001
Admission routes1
Has abstractyes

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