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Record W1482706990

Adaptations of Quaking Aspen for Defense Against Damage by Herbivores and Related Environmental Agents

2001· article· en· W1482706990 on OpenAlexfundno aff
Richard L. Lindroth

Bibliographic record

VenueUtah State Research and Scholarship (Utah State University) · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersRocky Mountain Research StationNatural Resources CanadaCooperative State Research, Education, and Extension ServiceNational Park ServiceU.S. Forest ServiceU.S. Geological SurveyOak Ridge National LaboratoryColorado State UniversityUniversity of WyomingFort Lewis CollegeUniversity of OxfordCanadian Forest ServiceCalifornia State Polytechnic University, PomonaU.S. Department of EnergyU.S. Fish and Wildlife ServiceU.S. Department of AgricultureNational Science Foundation
KeywordsHerbivoreBiologyDefence mechanismsChemical defenseAdaptation (eye)EcologyPlant defense against herbivoryBotanyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Quaking aspen (Populus tremuloides) employs two major systems of defense against damage by environmental agents: chemical defense and tolerance. Aspen accumulates appreciable quantities of phenolic glycosides (salicylates) and condensed tannins in most tissues and accumulates coniferyl benzoate in flower buds. Phenolic glycosides are toxic and/or deterrent to pathogens, insects, and small mammals, and coniferyl benzoate is toxic to ruffed grouse, but the functional significance of tannins remains unclear. Levels of secondary compounds are influenced by both genetic and environmental (e.g., resource availability) factors. Tolerance is less well understood but may play an important role as an adaptation to extensive damage during herbivore outbreaks. Critically needed is an assessment of the roles of chemical defense and tolerance in relation to the foraging ecology of large mammals such as cervids.

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 score0.038
Threshold uncertainty score0.681

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.297
Teacher spread0.239 · 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

Citations26
Published2001
Admission routes1
Has abstractyes

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