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Record W7055880885

Effects of livestock grazing on aquatic macroinvertebrates in Southern Interior wetlands of British Columbia, Canada

2015· article· en· W7055880885 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockWetlandSpecies richnessGrazingBiodiversityConservation grazingBiomass (ecology)InvertebrateWater quality
DOInot available

Abstract

fetched live from OpenAlex

Grasslands in the southern interior of British Columbia are extensively grazed by free ranging livestock. Water sources are limited in these grassland landscapes and wetlands are commonly used by livestock for drinking water and forage. My study examined the impacts of livestock disturbance on the abundance, biomass and community composition of aquatic macroinvertebrates residing in these wetlands. Aquatic macroinvertebrates were collected in the spring and summer of 2008 from 17 wetlands with a range of grazing disturbance. Three sweep and core samples were collected from each wetland and grazing intensity was determined by the amount of bare ground at each site. Spring sweep total abundance (r2=0.464, p=0.003) and biomass (r2=0.728, p<0.001) were negatively correlated with livestock disturbance as were spring abundance and biomass of zygopterans (r2=0.593, p<0.001; adj. r2=0.513, p=0.001). Spring sweep family richness (r2=0.462, p=0.003), Shannon’s family diversity (r2=0.569, p<0.001) and Simpson’s family diversity (r2=0.385, p=0.008) also decreased as livestock disturbance increased. Resource managers should consider Zygoptera (damselflies) as a potential indicator of wetland water quality and livestock impact. Range plans should adopt only light grazing in wetland areas and limit livestock access to sustain the biodiversity and productivity of these valuable aquatic ecosystems.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0960.000

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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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.

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
Published2015
Admission routes1
Has abstractyes

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