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Record W2162934534 · doi:10.1139/x00-167

Decomposition and nitrogen release from decomposing woody roots in coniferous forests of the Pacific Northwest: a chronosequence approach

2001· article· en· W2162934534 on OpenAlexvenueno aff
Chen Hua, Mark E. Harmon, Robert P. Griffiths

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersU.S. Department of AgricultureNational Science Foundation
KeywordsPinus contortaChronosequenceTsugaWestern HemlockWoody plantBark (sound)BotanyDouglas firForestryHorticultureBiologyEcological successionEcologyGeography

Abstract

fetched live from OpenAlex

Decomposition of woody roots in Sitka spruce (Picea sitchensis (Bong.) Carrière), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and ponderosa pine (Pinus ponderosa P. Laws. ex C. Laws.) dominated forests in Oregon, U.S.A. was studied using a chronosequence. Roots of five coniferous species were excavated from stumps with ages up to 46 years old. In order of increasing decomposition rate constant (k) the species were Douglas-fir < Sitka spruce < lodgepole pine (Pinus contorta Dougl. ex Loud.) < western hemlock (Tsuga heterophylla (Raf.) Sarg) < ponderosa pine. Variation in the proportion of bark, wood, and resin cores was correlated to these differences. Root wood showed the highest k, root bark the second, and resin cores the lowest. The occurrence of resin cores in woody roots of Douglas-fir, Sitka spruce, and lodgepole pine greatly slowed the decomposition of these species. White rots occurred frequently in ponderosa pine and lodgepole pine, whereas brown rots mostly appeared in Douglas-fir and Sitka spruce. Species with white rot had a higher k than those with brown rot. Decomposing woody roots started to release N after 20–30% mass loss, a point when the dead root C/N ratio averaged 140.

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 score0.748
Threshold uncertainty score0.950

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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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

Citations89
Published2001
Admission routes1
Has abstractyes

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