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Record W6922017115 · doi:10.1139/cjps-2014-015

Delayed inoculation of alfalfa with Sinorhizobium meliloti and Penicillium bilaiae

2015· article· en· W6922017115 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsnot available
Fundersnot available
KeywordsSinorhizobium melilotiInoculationMicrobial inoculantLegumeForageNitrogen fixationSinorhizobiumRhizobiaceae

Abstract

fetched live from OpenAlex

Knight, J. D. 2015. Delayed inoculation of alfalfa with Sinorhizobium meliloti and Penicillium bilaiae. Can. J. Plant Sci. 95: 205-211. The persistence of perennial forage legume crops relies on the establishment of an effective symbiotic relationship with the appropriate Rhizobium species and strain. Situations can arise where a forage legume crop fails to symbiotically fix N2. This study investigates if inoculation of alfalfa with a commercial Sinorhizobium meliloti inoculant 1 yr after seeding can induce biological N2 fixation at levels similar to those achieved when the inoculant is applied at seeding. Alfalfa (Medicago meliloti cv. Algonkwin) was grown at two sites in Saskatchewan and inoculated with S. meliloti or S. meliloti plus the P-solubilizing fungus Penicillium bilaiae. The inoculants were applied at seeding or applied 1 yr after seeding. Biological N2 fixation was measured in the fall of the delayed inoculation year as percentage of N derived from atmosphere (%Ndfa) using the 15N isotope dilution technique. Inoculation with S. meliloti increased %Ndfa at both sites relative to uninoculated and fertilized controls but had no effect on total N content or yield. Inoculating alfalfa the year after seeding increased %Ndfa relative to the controls at both sites, and at one of the sites %Ndfa in the delayed treatments was at the same levels measured in the year of seeding treatments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.257
GPT teacher head0.222
Teacher spread0.035 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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