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Record W6903452690 · doi:10.1139/cjps2012-128

The Biology of Invasive Alien Plants in Canada. 12. Pueraria montana var. lobata (Willd.) Sanjappa & Predeep

2013· article· en· W6903452690 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsKudzuPuerariaLobataInvasive speciesVineAlienPopulationPerennial plant

Abstract

fetched live from OpenAlex

Lindgren, C. J., Castro, K. L., Coiner, H. A., Nurse, R. E. and Darbyshire, S. J. 2013. The Biology of Invasive Alien Plants in Canada. 12. Pueraria montana var. lobata (Willd.) Sanjappa & Predeep. Can. J. Plant Sci. 93: 71-95. Kudzu, Pueraria montana var. lobata, is a perennial climbing vine known for its rapid and competitive growth. Introduced to North America and promoted at various times as a crop, an ornamental, and an erosion prevention tool, its negative impacts have been varied and severe in the United States. Dense populations overtop and smother crops and native vegetation, alter nitrogen cycles, and have the potential to affect air quality. Kudzu is winter-deciduous in North America with stems re-growing each season. In Canada, growth occurs from May until September, long enough for production of viable seed. Although widely believed to be intolerant of winter temperatures typical in eastern Canada, underground structures may be able to withstand temperatures as low as -30°C, and northward range expansion is predicted by climate change models. Dispersal in North America is primarily through intentional planting by humans, with clonal propagation and limited seed production and germination contributing to local population expansion. Only one population is known in Canada, near Leamington, Ontario. Once established, kudzu is difficult to eliminate or control without repeated actions. Efforts to prevent the movement and sale of kudzu in Canada, along with early detection and rapid response, monitoring, and education, offer potential strategies for control. Depending on the age, size and location of the population, herbicides, burning, mowing and grazing can be effective control measures.

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 score0.017
Threshold uncertainty score0.117

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 studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.231
GPT teacher head0.214
Teacher spread0.017 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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