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Record W7135076883 · doi:10.5376/ijmec.2025.15.0022

Ecosystem Engineering by Beavers: Impacts on Biodiversity and Hydrology

2025· article· W7135076883 on OpenAlexvenueno aff
Manman Li

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeaverWetlandBiodiversityHabitatEcosystem servicesEcological engineeringEcosystemRestoration ecologyNatural (archaeology)Adaptability

Abstract

fetched live from OpenAlex

This study systematically explores the combined impact of beaver activities on hydrological processes and biodiversity. The integration of hydrological model analysis and ecological monitoring cases reveals that beaver dam construction can effectively regulate water flow velocity and water level, enhance the exchange between surface water and groundwater, and improve water quality and sedimentation dynamics. Meanwhile, its transformation activities have created diverse habitats such as wetlands and ponds, promoting the diversity of aquatic organisms, terrestrial plants and birds, and providing microhabitats for some rare species. Regional cases further demonstrate that beaver projects not only enhance ecological services such as water storage, flood control and carbon sinks, but also to some extent trigger conflicts between agricultural production and infrastructure. Beavers are of great value in maintaining ecological functions, restoring degraded wetlands, and enhancing the adaptability of ecosystems to climate change. Scientific management and rational guidance for the coexistence of beavers and human systems can help provide natural solutions for wetland protection and ecological restoration. The research on beaver ecological engineering not only deepens scientists' understanding of species-environment interaction, but also provides theoretical support and practical cases for wetland protection and ecological restoration.

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 score0.026
Threshold uncertainty score0.612

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 head0.005
GPT teacher head0.195
Teacher spread0.191 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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