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Record W7048784542

Longevity of Turf Response to Agrium AT Slow and Controlled Release Fertilizers – 2012 trial

2018· report· en· W7048784542 on OpenAlexaff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typereport
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsAgricultural Research Institute of Ontario
Fundersnot available
KeywordsLongevityFertilizerShootControlled releaseVegetation (pathology)Nitrogen fertilizerDry weight
DOInot available

Abstract

fetched live from OpenAlex

The objective of this research project was to quantify turf response to a one-time application of coated urea fertilizers on Kentucky bluegrass turf on a soil rootzone. The longevity of turf response was compared between slow release and controlled release fertilizers. Turf performance was assessed for colour, quality, uniformity, and density through visual ratings as well as normalized-difference vegetation index (NDVI) readings. Shoot growth was measured through dry weights of clippings that were collected periodically. All treatments except one showed a significant improvement in colour and growth compared to the control plots. Fertilizer effects were seen within 10 days and persisted for the entirety of the 20 week data collection period. The average gain of fertilized treatments over control was 2.5 ranks in visual colour ratings or 0.12 units for NDVI. There were significant differences found between treatments in both strength of response and days to maximum release, again with the exception of one treatment.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.019
GPT teacher head0.225
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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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