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Record W2097799207 · doi:10.1890/es14-00438.1

Modeling the costs and benefits of seed scatterhoarding to plants

2015· article· en· W2097799207 on OpenAlexaff
Zhishu Xiao, Charles J. Krebs

Bibliographic record

VenueEcosphere · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsSeed dispersalBiological dispersalSeed predationBiologyPopulationSeed dispersal syndromeEcologyPredationMutualism (biology)

Abstract

fetched live from OpenAlex

Many plants interact with scatterhoarding animals as mutualists (seed dispersers) and antagonists (seed predators) simultaneously, but the net effects of scatterhoarding animals are rarely measured. In seed‐dispersal mutualisms, plant benefits (recruitment) received from dispersal agents should outweigh the costs, resulting in a relative fitness gain. Otherwise, plant populations cannot be sustained and would go extinct. Here we present a framework to quantify costs and benefits of scatterhoarding for animal‐dispersed plants and propose three models with the three separate scales (seed, tree and population) to quantify the costs and benefits for plants from scatterhoarding rodents. Since scatterhoarding is an adaptive dispersal strategy for many large‐seeded plants, tree‐ and population‐based models are needed to determine the costs and benefits for the plants. In the models presented here, all relevant parameters can be measured by regular surveys. Our tree‐ and population‐based models can be extended to seed plants that have dispersal agents other than scatter hoarding rodents.

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 score0.093
Threshold uncertainty score0.334

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 head0.034
GPT teacher head0.248
Teacher spread0.213 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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