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Record W1987140929 · doi:10.1139/x04-018

Harvest retention patches are insufficient as stand analogues of fire residuals for litter-dwelling beetles in northern coniferous forests

2004· article· en· W1987140929 on OpenAlexfundvenueno aff
Kamal J.K. Gandhi, John R. Spence, David W. Langor, Luigi E. Morgantini, Karen J Cryer

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHabitatLitterEcologyBiodiversityEnvironmental scienceCoarse woody debrisGeographyForestryBiology

Abstract

fetched live from OpenAlex

We compared litter-dwelling beetle assemblages of <1- to 2-ha unharvested coniferous patches embedded in 1-year-old clearcuts with beetle assemblages from <1- to 10-ha unburned fire residuals within 15- and 37-year-old burned forests. Our primary objective was to determine whether unharvested patches retain biotic elements that are similar to those of the surrounding uncut forests and to those of patches of forest skipped by wildfires. Beetle assemblages of the harvest residuals were similar to those of the uncut forest, suggesting that harvest residuals retain elements of the mature forest. However, beetle assemblages of harvest residuals differed from those of fire residuals. Thus, harvest residuals sited without regard to microhabitat characteristics or stand structure in fire residuals are insufficient analogues for the late successional habitats provided by fire residuals. There was no relationship between size of harvest residuals and either beetle catch or diversity. Beetle catches were higher in round harvest residuals, and a number of forest species also appeared to be aggregated in round residuals. Forest managers may preserve biotic elements of young uncut forest by leaving round harvest residuals in clearcuts; however, a closer habitat match between harvest and fire residuals is likely required to preserve and maintain landscape-level forest biodiversity.

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)0.003
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 score0.852
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.286
Teacher spread0.248 · 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

Citations65
Published2004
Admission routes2
Has abstractyes

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