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Record W2097232633 · doi:10.4141/cjps2012-092

The concentration of yeast assimilable nitrogen in Merlot grape juice is increased by N fertilization and reduced irrigation

2012· article· en· W2097232633 on OpenAlexafffundvenue
Kirsten Hannam, G.H. Neilsen, T. Forge, D. Neilsen

Bibliographic record

VenueCanadian Journal of Plant Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsVineyardIrrigationHuman fertilizationComposition (language)ChemistryHorticultureBerryNitrogenWine grapeWineAgronomyBiologyFood science

Abstract

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Hannam, K. D., Neilsen, G. H., Forge, T. and Neilsen, D. 2013. The concentration of yeast assimilable nitrogen in Merlot grape juice is increased by N fertilization and reduced irrigation. Can. J. Plant Sci. 93: 37–45. Vineyard management practices that can be used to elevate yeast-assimilable nitrogen (YAN) above the 140 mg N L −1 required for efficient fermentation are of critical interest. The effects of N fertilization and reduced irrigation frequency on grape juice YAN, fruit composition and yield were examined in a 5-yr study on Merlot (Vitis vinifera L.) vines. Fertilization with N increased the concentration of YAN in grape juice by improving grapevine N status as indicated by petiole N concentrations. Reduced irrigation frequency appeared to have no effect on grape juice YAN status but short-term reductions in the quantity of applied water during the early stages of berry development in 2 of the study years did increase YAN. Juice pH was sometimes increased by reduced irrigation and N application treatments, but levels remained acceptable for wine production. Other measures of fruit composition were less sensitive to irrigation and N fertilization treatments. Inter-annual variability played an important role in determining grape juice YAN, fruit composition and yield. Future work should focus on refining management practices, e.g., the timing of N application, to minimize the effects of annual variability on grape juice YAN concentrations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.240
Teacher spread0.214 · 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

Citations20
Published2012
Admission routes3
Has abstractyes

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