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Record W4402899610 · doi:10.5376/be.2024.14.0019

Building Ecosystems: The Transformative Role of Beavers

2024· article· en· W4402899610 on OpenAlexaffvenue
Dan Zhu, Shanshan Yu

Bibliographic record

VenueBiological Evidence · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsTransformative learningEcosystemEcosystem engineerEnvironmental ethicsEnvironmental resource managementEcologySociologyEnvironmental sciencePedagogyBiology

Abstract

fetched live from OpenAlex

Beavers ( Castor fiber and Castor canadensis ) are renowned ecosystem engineers whose activities significantly transform river corridors and wetlands. This study synthesizes current knowledge on the transformative role of beavers in ecosystem engineering, focusing on their impacts on hydrology, geomorphology, biogeochemistry, and biodiversity. Beaver dam construction alters water flow, increases surface and subsurface water storage, and modifies nutrient cycling, leading to enhanced habitat complexity and biodiversity. This study highlights the dual nature of beaver impacts, including both positive effects such as increased habitat heterogeneity and biodiversity, and negative consequences like localized flooding and vegetation death. The findings underscore the importance of considering beaver activities in river management and restoration practices to harness their ecosystem services while mitigating potential conflicts. This study aims to inform future research and management strategies as beaver populations continue to expand globally.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.256
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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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