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Record W1968182782 · doi:10.1111/afe.12069

Succession of saproxylic beetles associated with decomposition of boreal white spruce logs

2014· article· en· W1968182782 on OpenAlexafffund
Seung-Il Lee, John R. Spence, David W. Langor

Bibliographic record

VenueAgricultural and Forest Entomology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsBiologyEcological successionCoarse woody debrisTaigaEcologyFaunaHabitat

Abstract

fetched live from OpenAlex

Abstract We reared saproxylic beetles from 54 white spruce [ Picea glauca ( M oench) V oss] logs collected in northwestern A lberta, C anada. Logs represented six decay classes, ranging from freshly dead to well decayed. Beetle assemblages were indicated mainly by phloeophagous and predaceous species in early decay stages, although indicator species were mainly predaceous in later stages of decay. No indicator species were identified for intermediate decay stages. Larvae from rearings were disproportionately predaceous. Thus, movement of juveniles within and among coarse woody debris ( CWD) substrates is likely an important aspect of the life history for these species. Beetle assemblages changed progressively with increasing stages of decomposition. Assemblages of adjacent decay classes were highly similar, although similarity decreased with increasing difference in decay classes. Therefore, the retention of all decay classes of white spruce downed CWD on post‐harvest landscapes is necessary to conserve the associated saproxylic beetle fauna. Retention of CWD in advanced decay stages, which contains species not found in earlier decay classes, presents a particular challenge in forest management because of the long times required to develop CWD in the later stages of decomposition.

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 score0.015
Threshold uncertainty score0.235

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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

Citations39
Published2014
Admission routes2
Has abstractyes

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