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Heavy browsing by a mammalian herbivore does not affect fluctuating asymmetry of its food plants

2007· article· en· W2157091658 on OpenAlexafffundvenue
Dominique Berteaux, Brandee Diner, C. Dreyfus, Marion ÉBLÉ, Isabelle Lessard, Ilya Klvana

Bibliographic record

VenueEcoscience · 2007
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHerbivoreBiologyGrazingFluctuating asymmetryEcologyWoody plantMammal

Abstract

fetched live from OpenAlex

The only 3 published studies relating vertebrate herbivores to plant fluctuating asymmetry (FA) found significant correlations between grazing intensity and plant FA. The general value of these early findings is unclear, however, because FA studies are sensitive to selective reporting, the tendency to publish only a subset of studies that were undertaken. From 2000 to 2003 we quantified the correlations between past herbivory and plant FA in 3 plant–herbivore systems centred on a single mammal species, the North American porcupine (Erethizon dorsatum). We measured leaf FA in pairs of paper birch (Betula papyriferae; n = 24 pairs), quaking aspen ( Populus tremuloides; n = 25 pairs), and jack pine ( Pinus banksiana; n = 15 pairs) trees each containing a control (uneaten) and test (eaten) tree. Although damage incurred by trees from porcupine browsing was severe, we found no statistical association between plant FA and herbivory. We obtained this finding even though our study design did capture subtle variations in plant FA associated to plant genotype or year of sampling. Our study contrasts with earlier findings that plant FA is related to herbivory pressure. There may have been a publication bias as a result of selective reporting in this field of research. Therefore, replication (same hypothesis, same study system) and quasireplication (same hypothesis, different study system) are particularly important.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.423

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 studies0.0000.000
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 head0.042
GPT teacher head0.305
Teacher spread0.264 · 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 designBench or experimental
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

Citations8
Published2007
Admission routes3
Has abstractyes

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