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Record W1982003689 · doi:10.1139/cjb-2012-0269

Translocation of rare plant species to restore Garry oak ecosystems in western Canada: challenges and opportunities

2013· article· en· W1982003689 on OpenAlexafffundvenueabout
David R. Cléments

Bibliographic record

VenueBotany · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsTrinity Western UniversityWestern University
FundersParks CanadaU.S. Fish and Wildlife ServiceBotanical Society of America
KeywordsThreatened speciesEndangered speciesBiologyBiodiversityEcologyEcosystemWildlifeRestoration ecologyHabitat

Abstract

fetched live from OpenAlex

Covering just 2000 ha, Garry oak ecosystems (GOEs) in western Canada contain about 10% of the Species at Risk Act listed species at risk in Canada, including 30 plants listed by Committee on the Status of Endangered Wildlife in Canada as endangered. Since European settlement ca. 1840, GOE sites have been largely degraded by human disturbance, habitat fragmentation, invasive species, overgrazing, and fire suppression. A key strategy to mitigate this loss of biodiversity is to translocate rare plants to GOE restoration sites. The Garry Oak Ecosystem Recovery Team provides advice on proposed translocations but strongly encourages restoration practitioners to focus on plant populations already present on a site. There is a need for a closer look at challenges and opportunities afforded by translocation. If the approach taken is too precautionary, some rare species in this highly threatened ecosystem may be jeopardized. Current translocation efforts are being spearheaded by Parks Canada for golden paintbrush (Castilleja levisecta Greenm.), seaside birds-foot lotus (Lotus formossimus Greene), and white-top aster (Aster curtus Cronq.). Translocations like these together with further research on the genetics and ecology of rare plant species are critical to species recovery efforts within GOE and other similarly compromised ecosystems.

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 score0.659
Threshold uncertainty score0.880

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 head0.037
GPT teacher head0.194
Teacher spread0.157 · 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

Citations14
Published2013
Admission routes4
Has abstractyes

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