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Record W4388701161 · doi:10.1515/hf-2023-0057

Natural durability indicators in young plantation and native forest regrowth Gympie messmate (<i>Eucalyptus cloeziana</i>)

2023· article· en· W4388701161 on OpenAlexaff
Lesley Francis, Kate Semple, Babar Hassan, Jeffrey J. Morrell

Bibliographic record

VenueHolzforschung · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
FundersUniversity of the Sunshine CoastForest and Wood Products Australia
KeywordsHardwoodEucalyptusPithBark (sound)ForestryNative forestWood productionAgroforestryBotanyEnvironmental scienceHorticultureForest managementBiologyGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Understanding the durability of emerging plantation hardwood resources is important for optimising their production and use. This study compared timber density, extractives content and decay resistance in 12–13-year-old plantation and native forest regrowth Gympie messmate ( Eucalyptus cloeziana ) trees. Density increased from pith to bark for both plantation and native forest trees. Inner heartwood density of the plantation timber was significantly lower than that of the native forest regrowth timber. While the total extractives content of the outer heartwood was comparable in the plantation and native forest regrowth trees, the inner heartwood of the latter contained significantly greater extractives levels. Laboratory decay tests showed that all heartwood zones of plantation and native forest regrowth Gympie messmate were resistant to decay by the white rot Pycnoporus coccineus . The inner heartwood of the plantation timber was, however, susceptible to decay by the brown rot Fomitopsis ostreiformis . The results illustrate the potential variations in wood quality parameters to be considered when moving from native forest to plantation resources that are harvested at a younger age and managed for more rapid wood production.

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 score0.011
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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