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Record W6959303615 · doi:10.1139/cjss10004

Soil test phosphorus changes and phosphorus runoff losses in incubated soils treated with livestock manures and synthetic fertilizer

2011· article· en· W6959303615 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffLoamSoil waterPhosphorusManureFertilizerManure managementPhosphate

Abstract

fetched live from OpenAlex

Kumaragamage, D., Flaten, D., Akinremi, O. O., Sawka, C. and Zvomuya, F. 2011. Soil test phosphorus changes and phosphorus runoff losses in incubated soils treated with livestock manures and synthetic fertilizer. Can. J. Soil Sci. 91: 375-384. Source of phosphorus (P) and soil properties influence changes in soil test P (STP) concentrations and P runoff losses in manured and fertilized soils. We compared STP changes and P runoff losses in two soils, a clay loam and a sand, that were either unamended (control), or amended with liquid swine manure (LSM), solid cattle manure (SCM), or monoammonium phosphate (MAP) and incubated for 6 wk. Soil subsamples after incubation were analyzed for STP using Olsen (OP), Modified Kelowna (KP), Mehlich 3 (M3P) and water extraction (WEP) methods. We collected runoff from incubated soils for 60 min under a rainfall simulator, and analyzed for dissolved reactive P (DRP). Magnitude of STP increase in amended soils was greater in sand (19-48%) than in clay loam (7-37%). Increases in STP and DRP runoff concentrations in amended soils generally followed the order; MAP>LSM>SCM. Olsen P, KP and M3P were more accurate than WEP for predicting runoff DRP concentrations and loads, accounting for 43-49% of the variation in P concentrations for the first 30 min of runoff. Olsen P, the currently used STP method for environmental P regulation in Manitoba is sufficiently robust to predict runoff P losses from manure and fertilizer amended soils.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.202
Teacher spread0.081 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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