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Record W2001625179 · doi:10.4141/cjps08010

Above and below-ground competition between Kura clover (<i>Trifolium ambiguum</i>) and meadow bromegrass (<i>Bromus biebersteinii</i>): A greenhouse study

2009· article· en· W2001625179 on OpenAlexvenueaboutno aff
Jennifer Walker, J. R. King

Bibliographic record

VenueCanadian Journal of Plant Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyCompetition (biology)Perennial plantBromusBiologyShootPastureDry weightBiomass (ecology)PoaceaeEcology

Abstract

fetched live from OpenAlex

Kura clover (Trifolium ambiguum) is a perennial legume that shows potential for use in pasture mixtures in western Canada. Previous studies have shown that early growth of kura clover is reduced when grown in mixtures with grass species. A greenhouse study examined the relative importance of above- and below-ground competition on growth and development of kura clover when grown with a grass species. Kura clover plants were grown in pots with meadow bromegrass (Bromus biebersteinii) and barriers were put in place to remove all competition, shoot competition, root competition, or to allow full competition. After 12 wk, plants were harvested. Height, leaf area, leaf number, leaf dry weight, and root/crown dry weight were measured. Kura clover leaf number and leaf area were greatest when roots did not interact with meadow bromegrass roots. Vegetative biomass of kura clover doubled when there was no root competition. Shoot competition did not alter leaf number, leaf area, or leaf biomass. Successful establishment of kura clover is dependent on the reduction of root competition during the seedling phase. Measures taken to minimize the below-ground interaction should positively affect the yield potential of both species. Key words: Kura clover, meadow bromegrass, shoot competition, root competition

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)0.001
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 score0.385
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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

Citations17
Published2009
Admission routes2
Has abstractyes

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