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Nutrient Cycling in Soils: Sulfur

2011· book-chapter· en· W2169532600 on OpenAlexaff
John L. Kovar, Cynthia A. Grant

Bibliographic record

VenueASSA, CSSA and SSSA · 2011
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsNutrientSoil waterSulfurCyclingEnvironmental scienceSulfur cycleNutrient cycleCroppingSulfateEnvironmental chemistryAgronomyChemistrySoil scienceEcologyBiologyAgricultureGeography

Abstract

fetched live from OpenAlex

This chapter provides information on the demand for sulphur (S) in various cropping systems, what we know about the soil supply of S, the best ways of assessing S status and managing S inputs, and how all of this information can be put together to optimize crop production. Plants release measurable amounts of S into the atmosphere when S concentrations in foliage are high due to exposure to sulfur dioxide or excessive S uptake from soil. Microbial oxidation of reduced inorganic S forms, such as elemental S, sulfides, and thiosulfates, to sulfates is an important process in soils. The significant spatial and temporal variability in sulfate distribution creates problems in soil testing and subsequent recommendations for S fertilizers. Inter- and transdisciplinary efforts are necessary to unravel the interrelationships between S and other essential nutrients, and to understand their metabolic pathways within crop plants.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.011

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 head0.223
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations26
Published2011
Admission routes1
Has abstractyes

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Same venueASSA, CSSA and SSSASame topicNitrogen and Sulfur Effects on BrassicaFrench-language works237,207