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Record W2912782953 · doi:10.1080/10826076.2018.1564326

Measurement of phosphate in small samples using capillary electrophoresis with laser-induced luminescence detection

2018· article· en· W2912782953 on OpenAlexafffund
Jennifer R. Lischynski, Douglas M. Goltz, Douglas B. Craig

Bibliographic record

VenueJournal of Liquid Chromatography & Related Technologies · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryTetracyclineCapillary electrophoresisChromatographyLuminescencePhosphateCuvetteLinear rangeCapillary actionAnalytical Chemistry (journal)Detection limitBiochemistryOptics

Abstract

fetched live from OpenAlex

A capillary electrophoresis-based method was developed for the detection of phosphate in small samples. Phosphate was incubated in buffer with 50 μM Eu3+-tetracycline to allow for the formation of the luminescent Eu3+-tetracycline-phosphate complex. The complex was separated from the excess Eu3+-tetracycline, which is also luminescent, and the complex detected by post-column laser-induced luminescence within a sheath flow cuvette. Separation time was <90 s with a total run time of 5 min. No flushing of the capillary was required between runs. Phosphate standards ranging from 10–200 μM were analyzed. Increasing the concentration of Eu3+-tetracycline increased the linear range but at the cost of a decreased sensitivity. A decrease in the Eu3+-tetracycline resulted in an increase in sensitivity down to 5 μM but a decrease in linear range. As an application, the phosphate content of a milligram sample of soil was determined to be 0.51 ppm.

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)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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