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Record W2077584987 · doi:10.1139/x07-139

Modeling the decay of coarse woody debris in a subalpine Norway spruce forest of the West Carpathians, Poland

2008· article· en· W2077584987 on OpenAlexvenueno aff
Jan Holeksa, Tomasz Zielonka, Magdalena Żywiec

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoarse woody debrisSnagMontane ecologyResidence time (fluid dynamics)Environmental scienceForestryDebrisDecompositionFellingSubalpine forestPhysical geographyGeographyEcologyGeologyBiologyHabitatMeteorology

Abstract

fetched live from OpenAlex

Coarse woody debris (CWD) is an important structural element in forests. Its role depends on the species, size, position, and decay rate. This paper reports an attempt to determine the total residence time of CWD across all decay classes and also within successive decay classes. We simulated the process of CWD decomposition for stem size and position (snags versus logs), using matrices of the transition of CWD between decay classes. The study was based on a sample of 2785 Norway spruce snags and logs measured twice over a 10 year period in a Carpathian subalpine forest. The revealed pattern of decomposition depended highly on CWD size. When log numbers were considered, the mean total residence time increased from 71 years for small logs (diameter < 23 cm) to 90 years for medium-sized logs (23–35 cm), and to 113 years for large logs (>35 cm). In terms of volume, the mean total residence times of logs were 47, 49, and 63 years for the three diameter categories. Still shorter were the mean total residence times for log mass: 34, 41, and 41 years for the three diameter categories. The pattern of decomposition depended highly on the CWD attributes taken into consideration. The differences in decay rate between log diameter categories are considerable when the number of logs is taken into account, but they practically vanish when log mass is considered.

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 score0.824
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.254
Teacher spread0.193 · 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

Citations65
Published2008
Admission routes1
Has abstractyes

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