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Record W2159374428 · doi:10.1139/cjfas-2014-0105

Beaver impact on stream fish life histories: the role of landscape and local attributes

2014· article· en· W2159374428 on OpenAlexvenueno aff
Aneta Bylak, Krzysztof Kukuła, Józef Mitka

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeaverElectrofishingSTREAMSCastor canadensisMinnowSalmoEcologyPhoxinusBiologyHabitatFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The Eurasian beaver (Castor fiber) has been reintroduced into the Carpathian Mountains and has once again become an important factor for modifying streams. Forty-three sampling sites were compared with 10 reference sites in streams not settled by beavers. Models related to the impacts of beavers on various fish life history stages and a model presenting attributes influencing the ichthyofauna structure in streams with and without beavers were generated. Significant differences in the fish species composition were found between beaver ponds versus the running sections of streams. The changes associated with pond aging caused decrease of Siberian bullhead (Cottus poecilopus) density. For brown trout (Salmo trutta), beaver ponds were the only location where large individuals were found, while the upstream parts of the beaver complexes provided spawning habitat and an area for fry growth. Common minnow (Phoxinus phoxinus) and stone loach (Barbatula barbatula) had higher density in ponds than in streams. The decisive factors for the ichthyofauna in the mountain streams settled by beavers were local attributes related to beaver activity. Our results illustrate interactions among beaver, landscape context, and fish life history in influencing the response of the stream fish assemblages to beaver recolonization. It also helps answer the question of how Eurasian beaver influence stream fish assemblages, at a much larger scale than previous studies.

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 score0.021
Threshold uncertainty score0.999

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 studies0.0000.002
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 head0.010
GPT teacher head0.184
Teacher spread0.174 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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