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Record W2058252431 · doi:10.5558/tfc77713-4

Sheep grazing for vegetation management in the northern forests of British Columbia and Alberta

2001· article· en· W2058252431 on OpenAlexaffvenueabout
Erin Fraser, Richard Kabzems, Victor J. Lieffers

Bibliographic record

VenueThe Forestry Chronicle · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsGovernment of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsGrazingVegetation (pathology)Herbaceous plantAgroforestryStanding cropForestryGeographyForest managementEnvironmental scienceEcologyBiologyBiomass (ecology)

Abstract

fetched live from OpenAlex

Vegetation management treatments are applied on a large proportion of regenerated sites in British Columbia and Alberta to improve survival and early growth of conifers and to meet provincial standards. Conventional techniques like manual brushing and chemical herbicides continue to be widely applied. However, other less familiar methods like sheep grazing can also be a viable option on some sites. Sheep grazing has been demonstrated to offer good to excellent control of both herbaceous and woody vegetation, provided certain conditions are met. Specifically, the dominant vegetation must be palatable to the sheep, the large- and small-scale topography must be relatively even, the treatment must be carried out before the crop trees become severely suppressed or damaged and the animals must be effectively supervised. It is our position that there is an opportunity to increase the use of sheep grazing for vegetation control in some regions of the northern forests of British Columbia and Alberta, thus providing another viable option for forest managers. Key words: sheep, grazing, vegetation management, plantation establishment, conifer release, northern forest

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 score0.878
Threshold uncertainty score0.978

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Citations11
Published2001
Admission routes3
Has abstractyes

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