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Comparison of Ion-Specific Electrode and High Performance Liquid Chromatography Methods for the Determination of Iodide in Milk

2006· article· en· W1982775261 on OpenAlex
J. Melichercik, L. Szijarto, A.R. Hill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Dairy Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsChromatographyChemistryIon chromatographyIodideElectrodeIonInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Two methods for the determination of I- in raw and processed milk were examined. A simple ion-specific electrode (ISE) method was compared against a more complex HPLC reference technique. Accuracy and precision were evaluated both within and between the 2 methods. Both methods yielded good recoveries for Ion spiked samples, ranging from 87 to 114% for ISE and 91 to 100% for HPLC. Within-run repeatability and between-run reproducibility were superior with the HPLC method, but were still more than acceptable with the ISE technique. Overall agreement of paired results between ISE and HPLC methods was good (r2 = 0.85 on raw herd milk; r2 = 0.84 on processed milk). The ISE method had a significant positive bias relative to the HPLC reference method. Both methods lend themselves well to the measurement of I- in raw or processed milk. Given its relatively low cost and ease of use, the ISE method is well suited as a screening method. The impressive accuracy, precision, selectivity, and limit of detection of the HPLC technique make it an ideal confirmation method.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.320
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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