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Record W1802177349 · doi:10.2134/agronmonogr50.c7

Sulfur Response Based on Crop, Source, and Landscape Position

2008· book-chapter· en· W1802177349 on OpenAlexaff
Dave Franzen, Cynthia A. Grant

Bibliographic record

VenueAgronomy monograph/Agronomy · 2008
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSulfurCanolaSoil waterOrganic matterCropAgronomySulfateEnvironmental scienceBrassicaChemistryEnvironmental chemistrySoil scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Sulfur responses can be seen in almost any crop in the northern Plains of North America if soil sulfur availability is low. Canola (Brassica napus L.) is a crop grown in this region with special requirements for sulfur. Crops growing on soils with low organic matter that are coarse-textured are more susceptible to sulfur deficiency. Within fields, eroded hilltops and slopes, especially when consisting of coarse-textures, respond more to sulfur application than higher organic matter soils in depressional areas. In this region, sulfur source is important. Elemental sulfur, even if formulated with bentonite, does not alleviate sulfur deficiency as well as sulfate-containing amendments.

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 score0.009
Threshold uncertainty score0.031

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.003

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

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