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Record W3041115998 · doi:10.13031/aim.202000596

<i>Swine Manure: A Geochemical Perspective</i>

2020· article· en· W3041115998 on OpenAlexaboutno aff

Bibliographic record

Venue2020 ASABE Annual International Virtual Meeting, July 13-15, 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryBicarbonateAmmoniumAmmonium bicarbonateInorganic chemistryNitrogenPotassiumSodiumAmmonium chlorideEnvironmental chemistryMagnesiumEffluentSodium bicarbonateChlorideNutrientManureEnvironmental engineeringAgronomyEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract. Â This study characterized the solution chemistry found in seven swine manure storages in Saskatchewan, Canada in regard to the solution as a source term for potential contaminant transport study and modeling. The species of most concern in EMS seepage is nitrogen. Ammonium nitrogen is the most abundant form of nitrogen and one of the most abundant ions in the EMS effluent studied. Potassium and sodium may be cations of concern as they will likely compete with ammonium for soil exchange sites, which in turn will affect attenuation of ammonium. Bicarbonate and chloride are the most abundant anions (bicarbonate >> chloride). High concentrations of bicarbonate will likely affect precipitation of carbonaceous minerals and may affect solution pH. EMS ponds contain a solution composed of, as a percentage of total molal concentration, 36% ammonium, 36% bicarbonate, 8% potassium, 6% chloride, 5% sodium plus sulphate, calcium, magnesium and other nutrients. Additionally, the solution also contains approximately 6,000 mg/L organic and 9,000 mg/L inorganic carbon and has a near neutral pH. As a result, the solution has a low Eh resulting in nitrogen remaining in the ammonium-N form.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.006

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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Same venue2020 ASABE Annual International Virtual Meeting, July 13-15, 2020Same topicMineral Processing and GrindingFrench-language works237,207