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Record W6884620404 · doi:10.1139/cjps2012-122

In situ emergence timing of large and small crabgrass in residential turfgrass of southern Ontario

2013· article· en· W6884620404 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDigitaria sanguinalisDigitariaWeedLawnWeed controlGrowing season

Abstract

fetched live from OpenAlex

Turner, F. A. and Van Acker, R. C. 2013. In situ emergence timing of large and small crabgrass in residential turfgrass of southern Ontario. Can. J. Plant Sci. 93: 503-509. Large, Digitaria sanguinalis (L.) Scop., and small, Digitaria ischaemum (Schreb.) ex Muhl., crabgrass are problem weeds in turfgrass. Due to an increasing number of cosmetic pesticide bans in Canada there is a need to better understand the biology and ecology of crabgrass in order to develop and hone management approaches. The assessment of crabgrass recruitment timing is particularly relevant to its management, including the timing of alternative herbicide applications. This study focused on determining the emergence timing of established populations of large and small crabgrass in typical residential turfgrass stands in southern Ontario. Small crabgrass emerged earlier than large crabgrass at 346 and 515 growing degree days (GDD), respectively. In typical southern Ontario lawns both small and large crabgrass emerge after cool-season turfgrass has established and emergence continues late into the summer. For example, even within the last 2 wk of July we observed over 700 seedlings m-2 of large crabgrass emerging in some observation plots. This study also confirmed that small crabgrass emerges earlier than large crabgrass. There was a greater difference in emergence timing between species rather than among sites, suggesting that it is important to differentiate between species when timing management approaches. The late and prolonged emergence of crabgrass makes residential lawns that are not well maintained susceptible to infestation for a long portion of the growing season. This study also demonstrated that cumulative GDD may be a reliable measure for tracking crabgrass emergence suggesting, that it could be used as a tool for management, including the application of alternative herbicides. This study reinforces the importance of maintaining healthy and dense turf stands throughout the season as a deterrent to crabgrass infestations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.998

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)0.0030.000

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 head0.180
GPT teacher head0.231
Teacher spread0.051 · 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 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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