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Phosphorus fertilizer effects on the competition between wheat and several weed species

2009· article· en· W2092367279 on OpenAlexaff
Robert E. Blackshaw, Randall N. Brandt

Bibliographic record

VenueWeed Biology and Management · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsWeedBiologyCompetition (biology)FertilizerAgronomyPhosphorusCropWeed controlEcologyChemistry

Abstract

fetched live from OpenAlex

Information on phosphorus (P) fertilizer affecting crop–weed competitive interactions might aid in developing improved weed management systems. A controlled environment study was conducted to examine the effect of three P doses on the competitive ability of four weed species that were grown with wheat. Two grass and two broad‐leaved weed species were chosen to represent the species that varied in their growth responsiveness to P: wild oat (medium), Persian darnel (low), round‐leaved mallow (high), and kochia (low). Wheat and each weed species were grown in a replacement series design at P doses of 5, 15, and 45 mg P kg −1 soil. The competitive ability of the low P‐responsive species, Persian darnel and kochia, decreased as the P dose increased, supporting our hypothesis that the competitiveness of species responding minimally to P would remain unchanged or decrease at higher P levels. As expected, the competitiveness of the high P‐responsive species, round‐leaved mallow, progressively improved as the P dose increased. However, wild oat's competitive ability with wheat was not affected by the P fertilizer. The results suggest that fertilizer management strategies that favor crops over weeds might deserve greater attention when weed infestations consist of species known to be highly responsive to higher soil P levels. The information gained in this study could be used to advise farmers of the importance of strategic fertilizer management in terms of both weed management and crop yield.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.221

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations29
Published2009
Admission routes1
Has abstractyes

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