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Record W2256450866 · doi:10.1139/cjps-2014-0348

Effect of nitrogen fertilization on camelina seed yield, yield components, and downy mildew infection

2016· article· en· W2256450866 on OpenAlexafffundvenueabout
Yunfei Jiang, C. D. Caldwell

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsDalhousie University
FundersGenome Atlantic
KeywordsCamelina sativaCamelinaDowny mildewBiologyAgronomyCropYield (engineering)Randomized block designCrop yield

Abstract

fetched live from OpenAlex

Camelina [Camelina sativa (L.) Crantz] has potential in aquaculture, livestock feed production, and the biofuel industry. It is necessary to determine the appropriate production technology for the newly introduced crop under different environmental conditions. The objective of this 2-year study was to measure the response of five camelina genotypes in terms of seed yield, yield components, and disease incidence to applied nitrogen (N) at multiple sites in the Maritime provinces of eastern Canada. The factorial experiment was set up as a randomized complete block design. The two factors were six N rates (0, 25, 50, 100, 150, and 200 kg ha −1 N) and five genotypes of camelina (Calena, CDI002, CDI005, CDI007, and CDI008). The interactive effect of N rates and genotypes was considered. Results showed that camelina, which is usually considered a low-input crop, responded positively to increased applied N at rates up to 200 kg ha −1 N. Seed yield responded differently to applied N rates depending on genotype. Branch and pod development were decisive for seed yield. The advanced line CDI007 had the highest yield potential among the five genotypes. Downy mildew infection was positively correlated with applied N rates; however, seed yield was not significantly affected by downy mildew infection.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations32
Published2016
Admission routes4
Has abstractyes

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