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Record W831193469

Assessing the Effects of Juniperus virginiana Removal on Cattle Forage

2015· article· en· W831193469 on OpenAlexaboutno aff
Elise Jarrett

Bibliographic record

VenueLincoln (University of Nebraska) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsnot available
Fundersnot available
KeywordsForageEnvironmental scienceForestryAgronomyBiologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Over the past several decades, expanding eastern red cedar (Juniperus viginiana) populations have altered prairie and grassland ecosystems in the Great Plains, causing a reduction in livestock grazing capacity. This study examined the effects of eastern red cedar removal on cattle forage coverage located on private property in northeastern Nebraska. An analysis of collected field data obtained using a line intercept method in a control and an experimental plot was performed on Elymus canadensis (Canada wild rye), Poa pratensis (Kentucky bluegrass), and Bromus inermis (smooth brome), three common grassland species that serve as forage for cattle. Results show Canada wild rye responded negatively to the eastern red cedar removal, with a coverage declining from 58 cm in 2012 to 0 cm in 2014. Kentucky bluegrass initially responded negatively, declining from 1161 cm in 2012 to 217 cm in 2013, but responded positively in 2014, increasing to 414 cm. Smooth brome also experienced a similar response to cedar removal, declining from 736 cm in 2012 to 42 cm in 2013, but increasing to 234 cm in 2014. Statistical variances were not analyzed, which would help determine the magnitude of the effects the removal had on forage species, if any. Further data collection and analysis are needed to more accurately determine whether or not forage coverage will continue to increase and exceed the initial coverage recordings in 2012 with the Eastern red cedar present and provide an economic return for the ranchers.

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 score0.390
Threshold uncertainty score0.243

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 studies0.0000.000
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 head0.014
GPT teacher head0.221
Teacher spread0.208 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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