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Cattle grazing effects on plant species composition and soil compaction on rehabilitated forest landings in central interior British Columbia

2006· article· en· W2509497342 on OpenAlexaboutno aff
Reg F. Newman, C. Trethewey, Chuck Bulmer, Bill Chapman

Bibliographic record

VenueJournal of Soil and Water Conservation · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingSoil compactionPinus contortaForageTramplingEnvironmental scienceAgronomyCanopyBiologyAgroforestryForestryGeographySoil waterEcology

Abstract

fetched live from OpenAlex

Soils on forest landings (areas of cutblocks where harvested trees are processed and loaded onto trucks) are often degraded and unable to support optimal growth of planted conifers unless rehabilitation practices are applied. In British Columbia (BC), cattle often graze forage on forest landings. This study evaluated the effects of cattle grazing on native and non-native plant species composition, soil compaction, and tree growth on rehabilitated forest landings in the central interior of BC. Three study sites (landings) were rehabilitated by tillage in 1998, planted with lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) in 1999, and sampled during May-September 2003. Grazing regimes consisted of ungrazed exclosures and landings grazed to achieve 50 percent utilization of forages. Abundance of native species and non-native weeds was not affected by grazing, while alsike clover (Trifolium hybridum L.) was the only non-native forage species reduced by grazing. With greater mechanical resistance and less stable aggregates, the soil on the grazed landings was less favorable to plant growth. Canopy cover of lodgepole pine, tree height, diameter, and leader growth were all reduced on areas of the landing used by cattle. Trampling damaged 75 percent of trees, but 70 percent of planted lodgepole pine survived. Cattle grazing on rehabilitated landings may be feasible but managers should recognize the potential for cattle damage to regenerating tree seedlings and incorporate plans to prevent or mitigate such damage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.051
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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