MétaCan
Menu
Back to cohort
Record W4396708210 · doi:10.24124/2024/59496

Testing nitrogen and iron based compounds as environmentally safer alternative to control broadleaf weeds in turfgrass

2024· dissertation· en· W4396708210 on OpenAlexfundno aff
Simran Gill

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsLolium perenneAgronomyFestuca rubraWeed controlAmmoniumAmmonium sulfatePoa pratensisPerennial plantUreaChemistryTrifolium repensNitrogenBiologyPoaceae

Abstract

fetched live from OpenAlex

Turfgrass is an important component of urban and rural lawns and landscapes. However, broadleaf weeds such as dandelions (Taraxacum officinale Weber ex. F.H. Wigg) and white clovers (Trifolium repens L.) pose major challenges to the health and aesthetics of turfgrass fields. Traditional chemical weed control methods, such as 2,4-dichlorophenoxyacetic acid (2,4-D) herbicides, are commonly used, but their safety and environmental impacts are contentious. Seeking environmentally friendly alternatives, this research investigated the effectiveness of nitrogen and iron compounds as nutrient management methods for weed control. In a two-phase experiment; the first was conducted on a mix of cool season turfgrasses (included perennial ryegrass (Lolium perenne L.), Kentucky bluegrass (Poa pratensis L.) and creeping red fescue (Festuca rubra L.)) grown in plastic containers under controlled conditions in the greenhouse. The treatment application included individual nitrogen (1 = urea and 2 = ammonium sulphate) and iron (3 = chelated iron and 4 = iron sulphate) compounds and their combinations (5 = urea × chelated iron, 6 = urea × iron sulphate, 7 = ammonium sulphate × chelated iron, 8 = ammonium sulphate × iron sulphate) contrasted with 9 = a conventional synthetic herbicide (Killex) and a 10 = control (no application) treatment. Weekly assessments over a 12-week period revealed that the combination of ammonium sulphate × iron sulphate had overall best results for weed control and turfgrass quality indicators, and thus was the most effective in inhibiting the growth of dandelions and white clovers while improving the health of turfgrass. The second part, following the greenhouse studies, tested the efficacy of the ammonium sulphate × iron sulphate treatment versus Killex and a control (no application) treatment under natural open environmental conditions at two sites (site 1: no shade vs. site 2: partial shade) with existing broadleaf weeds. The ammonium sulphate × iron sulphate treatment combination resulted in significant reduction in weed cover (66% and 33% in sites 1 and 2, respectively) as well as yielded superior turfgrass quality (based on visual quality ratings and photosynthetic capacity recorded) as compared to both Killex and the control treatments. Overall, the results of this research demonstrate that the combination of ammonium sulphate × iron sulphate is a promising nutrient management solution capable of achieving both aesthetic goals of weed control and turfgrass quality.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.235
Teacher spread0.227 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicTurfgrass Adaptation and ManagementFrench-language works237,207