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Browsing of Antelope Bitterbrush(Purshia tridentata: Rosaceae) in the South Okanagan Valley, British Columbia: Age Preferences and Seasonal Differences

2000· article· en· W2172465034 on OpenAlexaffabout
Pam G. Krannitz, Samantha Hicks

Bibliographic record

VenueThe American Midland Naturalist · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British ColumbiaNatural Resources Canada
Fundersnot available
KeywordsTwigShrubBiologyForageDactylis glomerataOdocoileusSeedlingClipping (morphology)BotanyAgronomyHorticultureEcologyPoaceae

Abstract

fetched live from OpenAlex

We compared browsing on twigs of small and large antelope bitterbrush (Purshia tridentata) shrubs among ten sites in the south Okanagan valley, British Columbia. We tested whether there were any age preferences by browsers and determined whether these preferences changed between seasons and mode of browsing. Two different types of browsing were observed: leaf stripping which occurred in the summer and twig clipping which occurred predominantly in the winter. We calculated age and size relationships showing that shoot volume and especially stem diameter were good predictors of shrub age. Among the ten sites, clipping removed 0.02 to 15.7% of a shrub's total twig length and stripping removed leaves from 0 to 5.2% of total twig length. Observations suggested that California bighorn sheep (Ovis canadensis california) stripped antelope bitterbrush leaves in late summer, mule deer (Odocoileus hemionus hemionus) clipped twigs in the winter and cattle clipped twigs in the summer. Browsers preferred to clip twigs on smaller and hence younger antelope bitterbrush shrubs. In contrast, larger and older shrubs were preferred for leaf stripping. Since twig clipping was more prevalent than leaf stripping in antelope bitterbrush, overall preference for younger shrubs may lead to difficulties in seedling establishment in regions where it is heavily used as winter forage.

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 score0.235
Threshold uncertainty score0.973

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 studies0.0000.002
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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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

Citations9
Published2000
Admission routes2
Has abstractyes

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