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Record W4401381936 · doi:10.1139/cjps-2023-0163

Relationship of seeding rate to biological nitrogen fixation and seed production of red clover in Saskatchewan

2024· article· en· W4401381936 on OpenAlexafffundvenueabout
Dan Malamura, Bill Biligetu, Sean M. Prager

Bibliographic record

VenueCanadian Journal of Plant Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsUniversity of Saskatchewan
FundersGovernment of Saskatchewan
KeywordsSeedingAgronomyRed CloverBiologyNitrogen fixationFixation (population genetics)NitrogenChemistryGene

Abstract

fetched live from OpenAlex

In Saskatchewan, red clover ( Trifolium pratense L.) is often grown for seed production. However, there is no recognized or data supported seeding rate for seed production of red clover in Saskatchwan. The objectives of this study were to identify an optimal seeding rate for seed production and biological nitrogen fixation (BNF) and to examine their relationships under semi-arid conditions. This experiment was conducted under field conditions using six different seeding rates (0.5, 2.5, 4.5, 6.5, 8.5, and 10.5 kg ha −1 ) at Melfort and Clavet, SK in 2018 and 2019, and seed yield, biomass, plant density, and BNF were measured. In our study, red clover was able to compensate for the range of seeding rates between 2.5 and 10.5 kg ha −1 without seed yield loss and BNF reduction, whereas biomass production and BNF were higher at 4.5 kg ha −1 seeding rate than in 0.5 kg ha −1 at Melfort, but no seeding rate effects were found at Clavet. Regardless of location, biomass was always positively correlated with BNF. However, no association was found between seed yield and BNF. Our results suggest that 4.5 kg ha −1 is ideal for seed production, BNF and biomass production of red clover in Saskatchewan.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.999

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 studies0.0000.000
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 head0.040
GPT teacher head0.234
Teacher spread0.193 · 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

Citations0
Published2024
Admission routes4
Has abstractyes

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