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Record W4408417927 · doi:10.1002/its2.70015

Application of abscisic acid affects turf stand evapotranspiration and photosynthetic rates

2025· article· en· W4408417927 on OpenAlexaff
Alexandra Ficht, Craig Harnock, John Watson, E.M. Lyons

Bibliographic record

VenueInternational Turfgrass Society research journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAbscisic acidPhotosynthesisEvapotranspirationEnvironmental scienceAgronomyBotanyBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Water use by turfgrasses is important because it impacts land use decisions and the potential use of natural turfgrasses in enclosed stadia due to humidity levels and fan comfort. The application of exogenous abscisic acid (ABA) has been shown to reduce evapotranspiration (ET) and photosynthetic rates of plants. This study aimed to examine the impact of foliar ABA application on Kentucky bluegrass (KBG; Poa pratensis ) ET and photosynthetic rates. In addition, water use efficiency (WUE), turfgrass growth, and percent green cover before and after application were also measured. The application of ABA reduced KBG ET and photosynthetic rates by 19.2% and 18.2%, respectively, while also reducing turfgrass growth and greenness. Photosynthetic rates returned to normal during recovery periods, whereas ET rates did not return to pre‐drought levels. The response of ET and photosynthetic rates did not provide a consistent response, although WUE was reduced immediately after ABA application. These findings show reductions in ET rates that could affect conditions in indoor stadia, which may provide insight for turfgrass maintenance of indoor stadia with turfgrasses.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.025
GPT teacher head0.356
Teacher spread0.331 · 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 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
Published2025
Admission routes1
Has abstractyes

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