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Record W2038065098 · doi:10.1097/ss.0b013e3181602abc

NUTRIENT SUPPLY TO SOIL AND SURFACE WATER FROM DEPOSITION OF WIND-ERODIBLE-SIZED SOIL AGGREGATES

2008· article· en· W2038065098 on OpenAlexaff
Frauke Godlinski, Xiying Hao, Chi Chang, J. Lindeman

Bibliographic record

VenueSoil Science · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLoamDeposition (geology)Environmental scienceNutrientManureSoil waterAgronomyFertilizerHydrology (agriculture)Soil scienceChemistryGeologyBiology

Abstract

fetched live from OpenAlex

In regions subject to strong winds, considerable amounts of soil are transported off land and deposited to nearby fields and surface water. This study investigated the nutrient supply from deposition of erodible-sized soil to surrounding soil and surface water in a controlled laboratory setting. Wind-erodible fraction (WEF) aggregates were collected from a field with no manure or fertilizer application (Treatment WEF0) and a field that had received 180 Mg ha−1 year−1 of cattle manure (WEF180) for 30 years. The WEF aggregates were applied to a loamy sand soil and to distilled water at rates equivalent to 0, 10, 50, 100, and 150 Mg ha−1 and incubated for 2 years. Deposition of carbon and nutrient-enriched WEF aggregates increased the receiving soil's total carbon, nitrogen (N), and phosphorus (P) concentrations. The initially soil available N increased with WEF deposition rate, up to almost four times of the original value and by an additional 5 to 7 times after 2 years of incubation. Soil test P also increased with WEF deposition rates immediately after WEF application, but decreased over time as P immobilized to a less available form. After WEF was deposited to water, the initially soluble N and P increased up to 94 and 32 times, respectively, and did not reach a plateau in 2 years. Thus, the impact of WEF deposition on soil and water is long lasting and should be considered when developing farm management strategies or assessing environmental impacts of agricultural practices.

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 score0.004
Threshold uncertainty score0.007

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 communication0.0010.000
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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