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Record W2094082024 · doi:10.1071/ar04032

Effect of annual pasture composition, plant density, soil fertility and drought on vulpia (Vulpia bromoides (L.) S.F. Gray)

2004· article· en· W2094082024 on OpenAlexaff
P. M. Dowling, A. R. Leys, B. Verbeek, G. D. Millar, D. Lemerle, H. I. Nicol

Bibliographic record

VenueAustralian Journal of Agricultural Research · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsAgronomyPastureBiologySowingWeed controlPopulationPanicleWeed

Abstract

fetched live from OpenAlex

Vulpia is a widespread weed of temperate Australian pastures, and readily replaces more productive species. Short-term management of vulpia is possible with herbicides but densities rapidly increase in poorly competitive pastures after herbicide application. A field experiment at Wagga Wagga, NSW, examined the effect of 2 fertility levels and 4 pasture types [subterranean clover sown at 1, 25, 100 kg/ha, and subterranean clover (25 kg/ha) + annual ryegrass (20 kg/ha)] on 2 densities of vulpia (50,5 500 plants/m2) from 1990 to 1994. Initially vulpia plant density was inversely related to sowing rate of subterranean clover, but over time this effect declined as the subterranean clover populations converged. Presence of annual ryegrass always resulted in lower vulpia plant, panicle and seed densities compared with treatments where subterranean clover only was present. Respective densities per m2 in 1993 for the average of the subterranean clover monocultures and for annual ryegrass plus subterranean clover were: plant 1315 v. 265; panicle 6700 v. 130; seed 542 400 v. 3460. The effect of drought in 1994 and presence of annual ryegrass were shown to significantly lower the sustainable population of vulpia at Wagga Wagga from 5000–6000 to <1000 plants/m2. The short-term nature of herbicide application for control, and the need to ensure that competitive species were present to slow recruitment of vulpia in any long-term management strategy, were highlighted.

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 score0.776
Threshold uncertainty score0.407

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 integrity0.0000.001
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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