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Record W2072891962 · doi:10.2166/wst.2011.747

Nutrient export in run-off from an in-field cattle overwintering site in East-Central Saskatchewan

2011· article· en· W2072891962 on OpenAlexaffabout
Andy Smith, J.J. Schoenau, H.A. Lardner, Joshua Elliott

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsSnowmeltNutrientSurface runoffNitrateEnvironmental scienceSurface waterOverwinteringAgronomyPhosphorusWater qualityHydrology (agriculture)Soil waterAnimal scienceChemistryEcologyBiologyEnvironmental engineeringSoil scienceGeology

Abstract

fetched live from OpenAlex

Wintering cattle directly in the field creates potential concerns with water quality, as nutrients added from urine and fecal material over the winter can end up in runoff water, ground water and soil. In 2008/2009 an experiment was conducted to observe the effect of in-field winter feeding of cows on the nutrients in spring snowmelt run-off water. Low temperatures give little opportunity for organic N, urea and ammonium added in the urine and fecal matter to convert to nitrate, resulting in nitrate-N concentrations in snowmelt run-off water that were similar in the control and winter fed areas. Orthophosphate-P and ammonium-N concentrations were significantly elevated in run-off from the winter feed treatment basins compared to the controls. Surface soil sampled in the spring from the winter feeding site had higher soluble nitrate while soluble forms of phosphorus in the soil were lower compared to the fall soil samples. Caution should be used when utilizing in-field winter feeding systems so that the runoff water does not reach sensitive water bodies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.010
GPT teacher head0.207
Teacher spread0.198 · 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 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

Citations9
Published2011
Admission routes2
Has abstractyes

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