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Record W2076594633 · doi:10.1139/x10-149

The tree mortality regime in temperate old-growth coniferous forests: the role of physical damage

2010· article· en· W2076594633 on OpenAlexvenueno aff
Andrew J. Larson, Jerry F. Franklin

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersPacific Northwest Research StationNational Park ServiceU.S. Forest ServiceNational Science Foundation
KeywordsDiameter at breast heightBasal areaTemperate rainforestCoarse woody debrisTemperate climateForestryDisturbance (geology)BreakageForest dynamicsEcologyBiologyGeographyHabitat

Abstract

fetched live from OpenAlex

Aspects of the tree mortality regime were characterized for old-growth conifer forests in Mount Rainier National Park, Washington, USA, using individual tree (stems ≥5 cm diameter at breast height (dbh)) records from a network of permanent forest research plots. Average annual forest-wide mortality rates of trees ≥15 cm dbh never exceeded 1% on a stem-density or basal-area basis; mortality was slightly higher for stems <15 cm dbh. Physical agents of mortality (uprooting, stem breakage, and crushing by falling debris) accounted for approximately 40% and 45% of mortality events in trees <15 and ≥15 cm dbh, respectively. These physical processes were chronic sources of mortality: they were not associated with a single or few disturbance events. Preexisting decay fungi were associated with trees that died proximately due to stem breakage (41%) and uprooting (22%), consistent with a predisposing role of decay fungi in trees that die due to mechanical damage. Given the importance of physical processes in the tree mortality regime, we suggest that a richer mechanistic understanding of the causes and consequences of tree mortality in natural forests will be achieved with models that consider the physical, as well as the physiological, attributes of trees and forests.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.907
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.269
Teacher spread0.238 · 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

Citations91
Published2010
Admission routes1
Has abstractyes

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