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Record W2131418695 · doi:10.1111/nph.12019

Variation in carbon availability, defense chemistry and susceptibility to fungal invasion along the stems of mature trees

2012· article· en· W2131418695 on OpenAlexafffund
Devin W. Goodsman, Inka Lusebrink, Simon M. Landhäusser, Nadir Erbilgin, Victor J. Lieffers

Bibliographic record

VenueNew Phytologist · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsVariation (astronomy)BotanyChemistryCarbon fibersBiologyMathematics

Abstract

fetched live from OpenAlex

If carbon (C) sinks withdraw carbohydrates as they are transported along tree stems, carbohydrate availability may depend on local sink strength and distance from sources. Defenses, including monoterpenes--a major component of resin--limit the invasibility of pines. Since carbohydrate reserves fund monoterpene synthesis, we hypothesized that monoterpene concentrations in pine stems would decrease from the crown to the lower stem, and susceptibility to fungal infection would increase. Here, we measured carbohydrate and monoterpene concentrations along the stems of lodgepole pine trees (Pinus contorta var. latifolia) before inoculating with a blue-stain fungus at different heights. After 6 wk, we assessed tree responses to fungal infection based on lesion length and carbohydrate mobilization. Concentrations of carbohydrates and monoterpenes in the phloem before inoculation decreased with distance from the crown, whereas lesion lengths after inoculation increased. However, trees mobilized sugars in response to fungal infection such that carbohydrate reserves near lesions were similar at all heights. Despite C mobilization, the lower stem was more vulnerable than the upper stem. Consistent with predictions based on sink-source relationships, vulnerability occurred where carbohydrates were less available, and likely resulted from C withdrawal by sinks higher in the supply chain.

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 score0.017
Threshold uncertainty score0.999

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 science0.0000.000
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 head0.014
GPT teacher head0.229
Teacher spread0.214 · 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

Citations76
Published2012
Admission routes2
Has abstractyes

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