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Record W3089206132

Assessing sulfur fertilizer response in Ontario's corn, soybean, and winter wheat crops

2020· dissertation· en· W3089206132 on OpenAlexfundaboutno aff
Alex Sanders

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaOntario Agri-Food Innovation AllianceGrain Farmers of Ontario
KeywordsAgronomyFertilizerSulfurWinter wheatEnvironmental scienceBiologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Reduced sulfur deposition levels and increasing crop yields are causing sulfur nutrition concerns for Ontario corn, soybean, and winter wheat crops. Randomized complete block field trials were completed at 28 southern Ontario site-years in 2018 and 2019 to determine the incidence and extent of grain yield response to sulfur fertilization in these species. Sulfur was spring applied as a sulfate or thiosulfate fertilizer source. Corn and winter wheat displayed significant sulfur-induced yield increases at two of five and three of eight site-years, respectively, with soybean providing no definitive response across 15 experiments. Significant profitable response was realized at one corn and one winter wheat trial. However, economic analyses averaged across sites did not show significant profit from sulfur for any of the three crops. Ontario-specific diagnostic tools are needed to predict corn, soybean, and winter wheat field sites that will deliver profitable response to sulfur fertilization within a given season.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.416
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.237
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes2
Has abstractyes

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