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Record W2901924770 · doi:10.25071/ryr.v3i0.40455

Is Garlic Mustard (Alliaria Petiolata) Causing a Decline in Native Species’s Richness?: A Literature Review and Case Study on Long-Term Data Sets from Two Parks in Southwestern Ontario

2016· review· en· W2901924770 on OpenAlexaboutno aff
Harsimrat Rataul

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpecies richnessGeographyEcologySpecies diversityNational parkInvasive speciesDiversity (politics)Native plantIntroduced speciesBiology

Abstract

fetched live from OpenAlex

In the past two decades, research to identify the direct and indirect impacts of garlic mustard on the diversity and richness of native species present in North American forests has increased significantly. My literature review aims to assess the evidence for whether the presence of garlic mustard in a disturbed or an undisturbed area is likely to be the sole or major cause of declines in native plant species richness and diversity. I review the literature on the relative number of short-term and long-term research studies on garlic mustard ecosystem impacts, and note that only 11 studies out of a sample of 100 focus on the long-term impacts of garlic mustard. Findings from the literature review are applied to a case study, a long-term data set (1995-2009) examining the correlation between garlic mustard density and plant species richness in two parks in Southwestern Ontario: Point Pelee National Park and Rondeau Provincial Park. The goal of the case study is to compile previous field data and to assess whether garlic mustard density is negatively correlated with native plant species richness.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.535
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.003
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 head0.189
GPT teacher head0.442
Teacher spread0.253 · 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.

Study designSystematic review
Domainnot available
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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