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Record W7065508525

Evaluation of an integrated management approach for the control of purple loosestrife, Lythrum salicaria L., in southern Manitoba, biological control and herbicides

2000· other· en· W7065508525 on OpenAlexfundaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2000
Typeother
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTriclopyrBiological pest controlGlyphosateVegetation (pathology)Weed controlWetlandIntroduced speciesPerennial plant
DOInot available

Abstract

fetched live from OpenAlex

Purple loosestrife ('Lythrum salicaria' L.) is a European wetland perennial that was introduced to North America in the early 1800s. It forms large monodominant stands that are thought to adversely affect ecosystem dynamics wherever it occurs. Field cage experiments were conducted in a 2 ha stand of purple loosestrife at Netley-Libau marsh in southern Manitoba from 1996 to 1998 to evaluate the effectiveness of an Integrated Vegetation Management (IVM) strategy using single techniques (herbicides (glyphosate and triclopyramine) and classical biological control ('Galerucella calmariensis' L.)) and combinations of techniques (herbicides combined with classical biological control). Results of this study indicate that herbicides and classical biological control are compatible strategies for purple loosestrife management. Triclopyr amine would be effective in areas where native vegetation still persists while glyphosate may be effective for purple loosestrife control in areas where monospecific stands of adult purple loosestrife dominate. Based upon the results of this study, it is best to treat an area infested with purple loosestrife with a herbicide, leaving areas of refugia for biocontrol agents. Release of 'G. calmariensis' can be done at about the same time herbicides are applied or the year after herbicides are applied. (Abstract shortened by UMI.)

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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

Citations0
Published2000
Admission routes2
Has abstractyes

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