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Record W4406882824 · doi:10.1002/its2.189

Use of biosolids as a potential fertilizer for lawn turf

2025· article· en· W4406882824 on OpenAlexaffabout
Vighnesh Lakshmana Sukhu, Geovanna Cristina Zaro, E.M. Lyons, Adam Gillespie, K.S. Jordan

Bibliographic record

VenueInternational Turfgrass Society research journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLawnBiosolidsFertilizerEnvironmental scienceAgronomyEnvironmental engineeringBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Ensuring turfgrass health through an effective fertilization program is crucial for stress tolerance and overall vigor. The use of organic fertilizers, in particular biosolids, has emerged as a promising alternative to mitigate the negative impacts associated with traditional inorganic fertilizers and to offer a sustainable and eco‐friendly approach to maintaining turfgrass health. The objective of this research was to investigate the potential of biosolids as a sole or supplemental source of fertilizer to improve turfgrass health. Two field trials were conducted in Guelph, ON, from 2018 to 2020. Fertilizer treatments consisted of a negative control with no fertilizer, two inorganic controls, and three organic fertilizers, two of which were biosolids, applied via topdressing either two or three times per season. The results demonstrated that biosolids were an appropriate form of fertilizer to maintain turfgrass health when used as a supplement to synthetic fertilizers. The biosolids performed similarly to traditional inorganic fertilizers across multiple health parameters, suggesting they are a viable alternative for sustaining high turf 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.407
Teacher spread0.324 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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