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Record W2168544420 · doi:10.1139/z06-177

Simulated winter browsing may lead to induced susceptibility of willows to beavers in spring

2006· article· en· W2168544420 on OpenAlexvenueno aff
Annelies J. Veraart, Bart A. Nolet, Frank Rosell, Peter P. de Vries

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersKoninklijke Nederlandse Akademie van WetenschappenNederlands Instituut voor Ecologie
KeywordsBiologyHerbivoreShootDry matterBark (sound)SalicaceaeGrowing seasonAgronomyWillowWoody plantNutrientBotanyHorticultureEcology

Abstract

fetched live from OpenAlex

Browsing may lead to an induced resistance or susceptibility of the plant to the herbivore. We tested the effect of winter browsing by Eurasian beavers ( Castor fiber L., 1758) on food quality of holme willows ( Salix dasyclados Wimm.) in and after the following growth season. Shrubs were pruned in February, and new shoots from these (cut) shrubs were compared with those of untreated (uncut) ones in May and November. The shoots were analysed for dry matter, nitrogen, acid detergent fibre, and total phenolics. In May, the leaves from the cut treatment had a better food quality (more water, more nitrogen, and less phenolics) than those from the uncut one. There was in part also a systemic response, with lower total phenolics in both the cut and untreated parts of pruned shrubs (uncut–cut) than in the uncut shrubs. In November, we did not find significant differences in biochemistry of bark among cut, uncut, or uncut–cut treatments. These results are in accordance with a cafeteria experiment in the field: in May the beavers preferred shoots from the cut treatment, but in November they showed no preference. The results suggest that willows invest in compensatory growth rather than a defence response early in the regrowing phase.

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)0.000
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 score0.790
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.214
Teacher spread0.200 · 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

Citations18
Published2006
Admission routes1
Has abstractyes

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