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Record W7070907926

The relative effects of grazing by bison and cattle on plant community heterogeneity in northern mixed prairie

2014· dissertation· en· W7070907926 on OpenAlexafffund

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaParks Canada
KeywordsGrazingSpecies evennessPlant communityConservation grazingHerbivoreCattle grazingSpecies richnessRange (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

Since northern mixed prairies evolved under the influence of bison, grazing may be an important process maintaining diversity in these ecosystems. However, it is unclear whether grazing by cattle has the same ecological consequences as grazing by bison. I surveyed plant communities that were grazed at a range of intensities by bison or cattle, or were mechanically mowed. I used generalized linear mixed models and geostatistical techniques to evaluate the effects of grazing intensity and species of grazer on structural and floristic responses, and analysis of variance to evaluate the effects of mowing. I found that both grazing and mowing increased diversity and reduced evenness of the plant community. Spatial patterns of grazing were similar for bison and cattle, although bison created more discrete patches at the highest intensities of grazing. My results suggest that despite some differences in their selective preferences, the two species may have similar ecological effects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.196
Teacher spread0.188 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueMspace (University of Manitoba)→Same topicEcology and Vegetation Dynamics Studies→French-language works237,207→