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Coarse woody debris in Australian forest ecosystems: A review

2005· review· en· W1972298682 on OpenAlexaboutno aff
GEMMA WOLDENDORP, Rodney J. Keenan

Bibliographic record

VenueAustral Ecology · 2005
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSnagCoarse woody debrisForest floorWoodlandForest ecologyEnvironmental scienceRainforestBiomass (ecology)AgroforestryLitterForestryEcologyEcosystemGeographyHabitatBiology

Abstract

fetched live from OpenAlex

Abstract Coarse woody debris (CWD) is the standing and fallen dead wood in a forest and serves an important role in ecosystem functioning. There have been several studies that include estimates of CWD in Australian forests but little synthesis of these results. This paper presents findings from a literature review of CWD and fine litter quantities. Estimates of forest‐floor CWD, snags and litter from the literature are presented for woodland, rainforest, open forest and tall open forest, pine plantation and native hardwood plantation. Mean mass of forest floor CWD in Australian native forests ranged from 19 t ha−1in woodland to 134 t ha−1in tall open forest. These values were generally within the range of those observed for similar ecosystems in other parts of the world. Quantities in tall open forests were found to be considerably higher than those observed for hardwood forests in North America, and more similar to the amounts reported for coniferous forests with large sized trees on the west coast of the USA and Canada. Mean proportion of total above‐ground biomass as forest floor CWD was approximately 18% in open forests, 16% in tall open forests, 13% in rainforests, and 4% in eucalypt plantations. CWD can be high in exotic pine plantations when there are considerable quantities of residue from previous native forest stands. Mean snag biomass in Australian forests was generally lower than the US mean for snags in conifer forests and higher than hardwood forest. These results are of value for studies of carbon and nutrient stocks and dynamics, habitat values and fire hazards.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.012
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.306
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations99
Published2005
Admission routes1
Has abstractyes

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