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Record W7135078747 · doi:10.5376/ijmec.2025.15.0018

Mechanisms and Patterns of Seed Dispersal in Terrestrial Ecosystems

2025· article· W7135078747 on OpenAlexvenueno aff
Jiong Fu

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalBiodiversityAbiotic componentHabitatEcosystemSeed dispersalTerrestrial ecosystemClimate changeResilience (materials science)

Abstract

fetched live from OpenAlex

This study systematically analyzed the mechanisms and patterns of seed propagation in terrestrial ecosystems, covering abiotic factors such as wind, water, and gravity, as well as biological factors such as birds, mammals, insects, and humans. Research shows that although long-distance transmission (LDD) events are relatively rare, they play a key role in species colonization, gene flow and distribution expansion. The spatial and temporal patterns, landscape structures and environmental heterogeneity jointly shape the dynamics of seed propagation, and the seed propagation network usually presents modular and nested characteristics. Human activities, such as habitat fragmentation, climate change and invasive species, cause significant disturbances in the transmission process, threatening biodiversity and the resilience of ecosystems. This study aims to provide a systematic framework for understanding how seed dispersal shapes terrestrial ecosystems and offer a scientific basis for formulating effective protection and management strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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