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Record W2153783174 · doi:10.1139/x07-163

Wood density and stocks of coarse woody debris in a northwestern Amazonian landscape

2008· article· en· W2153783174 on OpenAlexvenueno aff
Kuo‐Jung Chao, Oliver L. Phillips, Timothy R. Baker

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersNatural Environment Research CouncilLeverhulme Trust
KeywordsCoarse woody debrisFloodplainAmazonianEnvironmental scienceAmazon rainforestForestryCarbon cycleDebrisWood productionEcologyHabitatGeographyAgroforestryBiologyForest managementEcosystem

Abstract

fetched live from OpenAlex

Coarse woody debris (CWD) is a rarely studied component of the carbon cycle. We report the first measurements of both CWD wood density and necromass in humid, lowland northwestern Amazonia, using both line-intersect and plot-based methods. Average CWD densities were not significantly different between clay-rich and white sand unflooded forests, but lower in floodplain forest (p ≤ 0.001). Necromass of CWD lying on the ground was also lower in the floodplain (10.3 ± 6.1 Mg·ha –1 , mean ± 1 SE) than in the clay-rich (30.9 ± 5.4) and white sand (45.8 ± 7.3) forests (p ≤ 0.001, using the line-intersect method). These patterns are likely driven by disturbance history, species composition, and decomposition rates. Plot-based data showed that standing and fallen CWD together accounted for 6.4%–15.4% of total coarse aboveground vegetative mass (trees ≥10 cm diameter). Across humid, lowland neotropical forests, we show that wood densities of intact and partially decayed CWD are significantly related with live wood density at the same site (p = 0.026 and 0.003, respectively). We show that these relationships can be applied generally to estimate CWD wood density for humid, lowland neotropical forests wherever destructive sampling is not possible.

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 score0.867
Threshold uncertainty score0.973

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 head0.049
GPT teacher head0.249
Teacher spread0.200 · 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

Citations72
Published2008
Admission routes1
Has abstractyes

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