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Record W2138428432 · doi:10.1139/x05-162

Characterization of damage and biotic factors associated with the decline of <i>Eucalyptus wandoo</i> in southwest Western Australia

2005· article· en· W2138428432 on OpenAlexvenueno aff
Ryan J. Hooper, K. Sivasithamparam

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsCankerCanopyBiologyCrown (dentistry)EucalyptusHorticultureBotany

Abstract

fetched live from OpenAlex

Crown decline of wandoo, Eucalyptus wandoo, in southwest Western Australia has escalated over the last 10 years, so very few unaffected stands remain. To assess the canopy-damage characteristics of trees in decline a destructive, partial-harvest method was used to sample branches in natural mixed-age stands. Necrosis of common cankers was closely associated with type-1 borer damage, characterized by "longitudinal" gallery structure on declining trees only. Cankers were found to be consistently more severe on declining trees, with decay regions affecting a greater proportion of sapwood tissue. Several infestations causing type-1 borer damage that varied in age were found on declining branches, providing evidence of cyclical damage events. Type-2 borer damage characterized by "ring-barking" gallery structure caused extensive damage in canopies, but was not always associated with decline. Interactions between foliage density and canker score showed that 17.8% and 63.1% of the variability in foliage-density ratios was accounted for in declining intermediate-health and unhealthy classes, respectively. The relationship was negligible for the healthy class (9.9%), providing strong evidence that cankers are causing foliage loss in declining canopies. Evidence suggests that an interaction between type-1 borer infestations and decay-causing fungi is responsible for the decline in E. wandoo wandoo canopies.

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)0.001
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 score0.825
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.283
Teacher spread0.240 · 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

Citations28
Published2005
Admission routes1
Has abstractyes

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