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Record W2028183410 · doi:10.4141/cjps07300

Invasive species issues in Canada - How can ecology help?

2007· article· en· W2028183410 on OpenAlexaffvenueabout
David R. Cléments, Paul M. Catling

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsAgriculture and Agri-Food CanadaTrinity Western UniversityWestern University
Fundersnot available
KeywordsInvasive speciesEcologyAlienConvention on Biological DiversityBiodiversityIntroduced speciesAlien speciesBiologyAgency (philosophy)GeographyPolitical scienceSociology

Abstract

fetched live from OpenAlex

The field of invasive species biology has been growing rapidly in the past decade, spurred on by the US Executive Order on invasive species in 1999. Despite calls to deal with invasive alien species under the International Convention on Biological Diversity in 1992, Canada has been slow to act. Part of the difficulty in managing alien invasive species effectively lies in the lack of ecological knowledge. The Canadian strategy on invasive alien terrestrial plants developed recently by the Canadian Food Inspection Agency sees research as a critical component of the strategy, including study of the biology and ecology of invasive plants. A symposium on ecology and invasive plant species at the Plant Canada meeting in 2007 in Saskatoon served to explore some emerging research in Canada, particularly focusing on Canada’s prairie region. Papers derived from five of the presentations are presented here and illustrate well the continuing challenge of applying ecological principles to the complex issues surrounding invasive plants. Canadian ecologists have made a significant contribution, but much remains to be done along the lines of the simple studies provided in this symposium. Key words: Invasive alien species, prairie region, biodiversity, ecological research

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)0.001
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 score0.197
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.191
Teacher spread0.163 · 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

Citations5
Published2007
Admission routes3
Has abstractyes

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