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Record W2175274235 · doi:10.1139/s07-053

Comparison between complete and partial recovery of N and P from stale human urine with MAP crystallization

2008· article· en· W2175274235 on OpenAlexvenueno aff
Zhan‐Guo Liu, Qingliang Zhao, Kun Wang, Wei Qiu, Wei Li, Jinfeng Wang

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
Fundersnot available
KeywordsStruviteCrystallizationMagnesiumPrecipitationStoichiometryPhosphorusPhosphateAmmoniumChemistryMolar ratioUrineRecovery rateNuclear chemistryChromatographyCatalysisBiochemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

To provide an option of magnesium ammonium phosphate (MAP) crystallization for different recovery objective and treatment aim of separated stale human urine, five lab-scale tests were conducted, among which two of them acted as complete N and P recovery and three of them as partial N and complete P recovery, respectively. The results showed the recovery efficiencies of PO43–-P were more than 85% when the molar ratio of Mg/N/P was above the needed stoichiometric proportion for MAP precipitation. Although higher recovery efficiencies of NH4+-N and PO43–-P could be obtained by adding more phosphate and magnesium salts during MAP precipitation, there existed disadvantages such as inconvenient operation, hard to be put into practice, high operation cost, etc. Thus it was not recommended to completely recover NH4+-N and PO43–-P simultaneously if there were no wastes containing high phosphorus on hand. All harvested precipitates were identified as nearly pure struvite with the presence of trace elements of K, Na, Ni, Mn, and Ca.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.203
Teacher spread0.189 · 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 designBench or experimental
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

Citations15
Published2008
Admission routes1
Has abstractyes

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