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Record W2028601512 · doi:10.1021/ed077p1619

ELISA and GC-MS as Teaching Tools in the Undergraduate Environmental Analytical Chemistry Laboratory

2000· article· en· W2028601512 on OpenAlex

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.

Bibliographic record

VenueJournal of Chemical Education · 2000
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSimazineAtrazineSample preparationEnvironmental analysisAnalyteSample (material)ChemistryEnvironmental chemistryChromatographyPollutantPesticide

Abstract

fetched live from OpenAlex

An undergraduate experiment for the analysis of potential water pollutants is described. Students are exposed to two complementary techniques, ELISA and GC-MS, for the analysis of a water sample containing atrazine, desethylatrazine, and simazine. Atrazine was chosen as the target analyte because of its wide usage in North America and its utility for students to predict environmental degradation products. The water sample is concentrated using solid-phase extraction for GC-MS, or diluted and analyzed using a competitive ELISA test kit for atrazine. The nature of the water sample is such that students generally find that ELISA gives an artificially high value for the concentration of atrazine. Students gain an appreciation for problems associated with measuring pollutants in the aqueous environment: sensitivity, accuracy, precision, and ease of analysis. This undergraduate laboratory provides an opportunity for students to learn several new analysis and sample preparation techniques and to critically evaluate these methods in terms of when they are most useful.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.084
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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