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Record W2025921557 · doi:10.1556/aagr.55.2007.1.6

Competition, light quality and seedling growth of Russian wildrye grass (Psathyrostachys juncea)

2007· article· en· W2025921557 on OpenAlexaff
P. G. Jefferson, R. B. Muri

Bibliographic record

VenueActa Agronomica Hungarica · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsTiller (botany)SeedlingBiologyFar-redShadingDry weightAgronomyLight intensityHorticultureBotanyRed lightPhysics

Abstract

fetched live from OpenAlex

The low seedling vigour of Russian wildrye grass ( Psathyrostachys juncea ) (RWR) limits its use. Shading from durum wheat ( Triticum turgidum ) reduced RWR leaf number, tiller number, leaf area and seedling dry weight in a growth room experiment. Treatments with similar shading differed in tiller number and dry weight, which suggested that light quality may have also contributed to these responses. In a second growth room experiment, light intensity (PAR) and red:far-red light ratio (670:730 nm) were altered by coloured plastic filters suspended above seedlings of Russian wildrye, crested wheatgrass ( Agropyron desertorum ) (CWG) and Dahurian wildrye grass ( Elymus dahuricus ) (DWR). Leaf area, tiller number and dry weight of RWR seedlings were reduced by declining red:far-red light ratio while light intensity differences at similar red:far-red ratio did not affect these variables. CWG exhibited similar responses to declining red:far-red light ratio as RWR, except that it exhibited a seedling weight response to light intensity. DWR tiller number was not responsive to low red:far-red light ratio but rather to low light intensity. However, DWR seedling weight, tiller weight and leaf area were responsive to declining red:far-red light ratio. These results indicate that RWR seedlings are sensitive to light quality changes caused by neighbouring plants.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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 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

Citations31
Published2007
Admission routes1
Has abstractyes

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