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Record W1994024406 · doi:10.1081/css-200049466

Temperature, Soil Moisture, and Antecedent Sulfur Application Effects on Recovery of Elemental Sulfur as SO<sub>4</sub>‐S in Incubated Soils

2005· article· en· W1994024406 on OpenAlexaff
E. D. Solberg, S. S. Malhi, M. Nyborg, K. S. Gill

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food CanadaAgriculture Food and Rural Development
Fundersnot available
KeywordsChernozemUdic moisture regimeSoil waterMoistureChemistrySulfurIncubationWater contentFertilizerElemental analysisAgronomySoil scienceEnvironmental chemistryEnvironmental scienceLoamGeology

Abstract

fetched live from OpenAlex

Abstract The oxidation of elemental sulfur (S) to plant‐available SO4‐S is influenced by several factors. Experiments were conducted to compare the recovery of applied elemental S as SO4‐S in Dark Gray Chernozem (Boralfic Boroll), Black Chernozem (Udic Boroll), and Gray Luvisol (Boralfs) soils incubated at different temperature, moisture, and antecedent elemental S application conditions. For all soils and incubation periods, the recovery of SO4‐S increased with temperature from 6 to 36°C and with soil moisture from 40 to 90% field capacity (FC). At low temperature and soil moisture, the recovery of SO4‐S increased relatively steadily with incubation time up to 30 days, but at optimum temperature and soil moisture, the SO4‐S recovery was relatively faster at the start of the incubation and declined as incubation progressed. Previous addition of elemental S to soil accelerated the SO4‐S recovery. The rate of SO4‐S recovery was also influenced by soil type. The findings suggest that the amount of elemental S fertilizer needed to meet the crop requirements should be adjusted based on temperature, soil moisture, previous elemental S application history, and soil type.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.595

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 communication0.0000.000
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 head0.007
GPT teacher head0.254
Teacher spread0.248 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCommunications in Soil Science and Plant AnalysisSame topicNitrogen and Sulfur Effects on BrassicaFrench-language works237,207