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Record W2012842720 · doi:10.1155/2013/457698

Sustaining Cavity-Using Species: Patterns of Cavity Use and Implications to Forest Management

2013· article· en· W2012842720 on OpenAlexaff
Fred L. Bunnell

Bibliographic record

VenueISRN Forestry · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSnagLoggingDead treeForest managementEcologyWood productionRange (aeronautics)Dead woodGeographyMammalAgroforestryForestryBiologyBiodiversityHabitatEngineering

Abstract

fetched live from OpenAlex

Many bird and mammal species rely on cavities in trees to rear their young or roost. Favourable cavity sites are usually created by fungi, so they are more common in older, dying trees that are incompatible with intensive fiber production. Forestry has reduced amounts of such trees to the extent that many cavity-using vertebrates are now designated “at risk.” The simple model of cavity use presented helps unite research findings, explain patterns of use, and clarify trade-offs that can, or cannot, be made in snag management. Predictions generated are tested using data from over 300 studies. Implications to forest management are derived from the tests, including the following: ensure sustained provision of dying and dead trees, retain both conifers and hardwoods and a range of size and age classes, sustain a range of decay classes, ensure that some large trees or snags are retained, promote both aggregated and dispersed retention of dead and dying trees, meet dead wood requirements for larger species where intensive fibre production is not emphasized, do not do the same thing everywhere, and limit salvage logging after tree mortality. The paper focuses on species breeding in the Pacific Northwest, but draws on data from throughout those species’ ranges.

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)0.000
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 score0.002
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.040
GPT teacher head0.225
Teacher spread0.185 · 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

Citations55
Published2013
Admission routes1
Has abstractyes

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