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Differential impact of vertebrate and invertebrate herbivores on the reproductive output of<i>Hormathophylla spinosa</i>

2000· article· en· W81874022 on OpenAlexvenueno aff
José M. Gómez, Regino Zamora

Bibliographic record

VenueEcoscience · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersUniversidad de Granada
KeywordsHerbivoreBiologyEcologyPredationReproductive successPopulationPlant reproductionPlant tolerance to herbivorySeed predationPollinationSeed dispersalPollen

Abstract

fetched live from OpenAlex

We analyzed herbivory on the reproductive structures (flowers, fruits, and seeds) of Hormathophylla spinosa (Cruciferae) by highly dissimilar species of herbivores, from ungulates to endophagous seed predators and gall-formers, in three different populations from 1988 to 1992. Specific objectives of the study were to quantify (i) the intensity of herbivory on reproductive structures of H. spinosa and the consequent loss of reproductive output; (ii) the frequency of herbivory, and how herbivory varied between years on the same individuals; (iii) spatial, among-population variation in herbivory intensity; and (iv) the relative importance of each herbivore species with respect to the losses in plant reproductive output. All plants of H. spinosa (n = 80) were heavily attacked by herbivores in the high mountains of the Sierra Nevada, where most plants were attacked by different types of herbivores during each year of the study. Plants lost about 50% of their reproductive structures during the study period. However, there was a pronounced difference in the magnitude of damage produced by each herbivore since ungulates accounted for most of the damage, whereas other herbivores caused much lighter damage. Mammalian herbivores appear to be a major ecological factor determining seed production in populations of H. spinosa.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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.

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
Published2000
Admission routes1
Has abstractyes

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