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Record W2104276573 · doi:10.1111/1442-1984.12051

Post‐dispersal seed predation and its relations with seed traits: a thirty‐species‐comparative study

2014· article· en· W2104276573 on OpenAlexfundno aff
Hede Gong, Chaozhi Tang, Bo Wang

Bibliographic record

VenuePlant Species Biology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsPredationBiologySeed predationSeedlingSeed dispersalSeed dispersal syndromeForagingGerminationBiological dispersalEcologyAgronomyBotanyPopulation

Abstract

fetched live from OpenAlex

Abstract Post‐dispersal seed predation is a key process determining the variability in seed survival in forests, where most seeds are handled by rodents. Seed predation is thought to affect seedling regeneration, colonization ability and spatial distribution of plants. Basic seed traits are the essential factors affecting rodent foraging preferences and thus seed survival and seedling recruitment. Many studies have discussed several seed traits and their effects upon seed predation by rodents. However, the results of those previous studies are usually equivocal, likely because few seed traits and/or plant species tend to be incorporated into these studies. In order to elucidate the relationships between seed predation and seed traits, we surveyed the predation of 48 600 seeds in a natural pine forest, belonging to 30 species, for three consecutive years. The results demonstrated that: (i) seed size and seed coat hardness did not significantly affect seed predation; (ii) total phenolics had a negative effect upon seed predation; (iii) positive effects of nitrogen content upon seed predation were found. From our study, it seems that the better strategy to prevent heavy predation is for plants to produce seeds with higher total phenolics content rather than physical defenses (i.e. hard seed coat) or larger seeds. Additionally, rodent foraging preference may depend more on N itrogen content than other nutrient content of seeds.

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 score0.036
Threshold uncertainty score0.986

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)0.0010.000

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 head0.024
GPT teacher head0.244
Teacher spread0.220 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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