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Beavers as ecosystem engineers – a review of their positive and negative effects

2018· review· en· W2901044152 on OpenAlexaboutno aff
I O Rozhkova-Timina, В К Попков, Peter Mitchell, Sergey N. Kirpotin

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsBeaverEcosystemEnvironmental scienceBiodiversityEcosystem engineerVegetation (pathology)Castor canadensisEcologyEcosystem servicesFlooding (psychology)Aquatic ecosystemWetlandHydrology (agriculture)BiologyGeology

Abstract

fetched live from OpenAlex

The paper reviews the environmental activity of beavers ( Castor fiber L. and Castor canadensis L.) and their impact on different aspects of the environment. Beavers inhabit almost all climatic zones but are most abundant in Russia, USA, and Canada. Beavers' ecosystem engineering activities include building dams and creating ponds. The dams provoke hydrological alteration, soil overwetting, changes in local micro- and nanorelief. The water stagnation in beaver ponds below dams results in lack of oxygen, a high carbon concentration, and the death of many aquatic organisms. The flooding water above the dam causes vegetation death due to overwetting and at the same time a rise in the biodiversity of water organisms. This paper includes original data gathered by Tomsk State University on lack of oxygen and subsequent fish death in dammed ponds. All environmental changes are cumulative and have strong contextual dependence. The beavers' environmental activity has positive and negative consequences.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations31
Published2018
Admission routes1
Has abstractyes

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