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Record W2048416149 · doi:10.1139/x07-205

Effects of stem anatomical and structural traits on responses to stem damage: an experimental study in the Bolivian Amazon

2008· article· en· W2048416149 on OpenAlexvenueno aff
Claudia Romero, Benjamin M. Bolker

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersUnited States Agency for International Development
KeywordsXylemBiologyBark (sound)PithBotanyPhloemEcology

Abstract

fetched live from OpenAlex

Persistence of tree species in a habitat depends on their ability to avoid and respond to disturbance-related damage. Responses to stem damage vary among species but typically include bark wound closure and prevention of xylem decay spread. These responses are associated with anatomical, structural, and physiological traits. This study explores how xylem (vessel size and (or) abundance, parenchyma abundance, ray width, and wood density) and phloem (bark thickness, proportion of live inner bark, ray width and (or) dilation, inter-ray distance, and tissue density) traits relate to responses to stem damage in seven species from the Bolivian Amazon. Rates of bark wound closure and radial xylem decay penetration were compared 2 years after experimental damage. A species that closed bark wounds rapidly (100% in Chorisia speciosa A. St.-Hil.) was not efficient at constraining xylem radial decay spread (1.7 mm). The opposite was true for Pseudolmedia laevis (Ruiz & Pav.) J.F. Macbr., a species that closed wounds slowly (30%) but efficiently controlled decay spread (0.5 mm). The relationship between anatomical and (or) structural traits and damage response variables revealed that species with favorable traits for rapid wound closure (e.g., widely dilating rays) had traits that favored xylem decay spread (e.g., low wood density). It is plausible that this apparent trade-off is based on physiological and phylogenetic constraints.

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 score0.161
Threshold uncertainty score0.850

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.321
Teacher spread0.283 · 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

Citations86
Published2008
Admission routes1
Has abstractyes

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