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Record W2022044468 · doi:10.2134/jeq2013.03.0074

Phosphorus Fractions in Solid and Liquid Separates of Swine Slurry Separated Using Different Technologies

2013· article· en· W2022044468 on OpenAlexafffund
Darshani Kumaragamage, O. O. Akinremi, Lorne Grieger

Bibliographic record

VenueJournal of Environmental Quality · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsGenome PrairieUniversity of ManitobaUniversity of Winnipeg
FundersWestern Economic Diversification Canada
KeywordsFractionationManureSlurryPhosphorusFlocculationChemistryFertilizerLiquid manureCentrifugationManure managementChromatographyAgronomyEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Solid-liquid separation is a manure management option whereby P-rich solid is separated from N-rich liquid, allowing the separated liquid to be used as a fertilizer without oversupplying P. Little information is available on how the different P fractions in manures are partitioned to solid and liquid during separation. We examined the distribution of various P fractions in liquid and solid separates of swine manure, separated using different techniques, to gain information useful for making choices regarding the optimum use of manure separates. Samples of raw manure (RM) and their separated solid (SS) and liquid (SL) were obtained using three different separation techniques: (i) centrifugation without flocculant (CNF), (ii) centrifugation with a flocculant (CFL), and (iii) rotary press with a flocculant (RFL). These were subsequently analyzed for P using a modified Hedley fractionation scheme. Only a small proportion of RM, ranging from 5 to 12%, was recovered in SS, an advantage if SS is to be transported off-site. Concentrations of molybdate-reactive P and total P in all P fractions were less in SL than in the corresponding RM on a fresh-weight basis. The separation index (percentage partitioned to SS) for total labile P (water-extractable + NaHCO-extractable P) was 63, 81, and 75% for CNF, CFL, and RFL, respectively. The proportion of total P in labile form was significantly lower in SL than in RM. Therefore, using SL as a fertilizer instead of RM may help to avoid excessive buildup of soil test P with manure applications.

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 score0.386
Threshold uncertainty score0.773

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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