MétaCan
Menu
Back to cohort
Record W174891288

Influence of Stem Cutting and Glyphosate Treatment of Lonicera maackii, an Exotic and Invasive Species, on Stem Regrowth and Native Species Richness

2005· article· en· W174891288 on OpenAlexaboutno aff
Henry Owen, April Lyn McDonnell, A. M. Mounteer, Brent L. Todd

Bibliographic record

VenueThe Keep (Eastern Illinois University) · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsnot available
FundersEastern Illinois UniversityNational Science Foundation
KeywordsHoneysuckleInvasive speciesIntroduced speciesBiologyGlyphosateCanopySpecies richnessNative plantBotanyEcology
DOInot available

Abstract

fetched live from OpenAlex

Lonicera maackii (Rupr.) Herder (Caprifoliaceae), Amur honeysuckle, is an exotic and invasive species in the United States that has quickly overtaken disturbed habitats in the eastern and midwestern United States, as well as in Ontario, Canada. A reduction of light due to its dense canopy, extended growing season compared to native species, and production of numerous basal sprouts allow L. maackii to outcompete its native counterparts. Eradication of this species can be difficult and time-consuming. This research was undertaken to identify how L. maackii influences species diversity and species re-establishment and to determine an efficient and effective eradication method. A study was designed to determine if L. maackii inhibited species diversity, if the removal of L. maackii would increase species diversity by reopening the canopy, and if mechanical removal or mechanical removal coupled with glyphosate treatment could be used effectively for its long-term eradication. It was found that L. maackii removal increased species diversity, and mechanical removal coupled with the application of glyphosate is an effective and relatively simple method for eradicating L. maackii, while mechanical stem removal alone simply delayed its growth.

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 score0.038
Threshold uncertainty score0.286

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 head0.044
GPT teacher head0.190
Teacher spread0.146 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Keep (Eastern Illinois University)Same topicBotany, Ecology, and Taxonomy StudiesFrench-language works237,207