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Record W1912108301 · doi:10.1002/jsfa.7238

Evaluating the use of plant hormones and biostimulators in forage pastures to enhance shoot dry biomass production by perennial ryegrass (<i>Lolium perenne</i> L.)

2015· review· en· W1912108301 on OpenAlexaff
Mohammad Zaman, Leonid V. Kurepin, W.D. Catto, Richard P. Pharis

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsLolium perenneAgronomyPerennial plantPastureDry matterShootForageBiomass (ecology)GrazingBiologyLoliumEnvironmental sciencePoaceae

Abstract

fetched live from OpenAlex

Fertilisation of established perennial ryegrass forage pastures with nitrogen (N)-based fertilisers is currently the most common practice used on farms to increase pasture forage biomass yield. However, over-fertilisation can lead to undesired environmental impacts, including nitrate leaching into waterways and increased gaseous emissions of ammonia and nitrous oxide to the atmosphere. Additionally, there is growing interest from pastoral farmers to adopt methods for increasing pasture dry matter yield which use 'natural', environmentally safe plant growth stimulators, together with N-based fertilisers. Such plant growth stimulators include plant hormones and plant growth promotive microorganisms such as bacteria and fungi ('biostimulators', which may produce plant growth-inducing hormones), as well as extracts of seaweed (marine algae). This review presents examples and discusses current uses of plant hormones and biostimulators, applied alone or together with N-based fertilisers, to enhance shoot dry matter yield of forage pasture species, with an emphasis on perennial ryegrass.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.389
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.322
Teacher spread0.247 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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