MétaCan
Menu
Back to cohort
Record W2573168343 · doi:10.5539/ijc.v9n1p58

Development of Spectrophotometric Method for the Determination of Atenolol in Normoten Drug

2017· article· en· W2573168343 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicAnalytical Methods in Pharmaceuticals
Canadian institutionsnot available
FundersAlbaha University
KeywordsAtenololChemistryAbsorbanceChromatographyMolar absorptivityCorrelation coefficientActive ingredientDrugDosage formLinear regressionPharmacology

Abstract

fetched live from OpenAlex

This research aimed to develop inexpensive, safe, rapid, efficent spectrophotometric method for the assay of atenolol in some antihypertensive drugs namely Normoten in its pharmaceutical formulation. The studied method is depend on the reaction of the drug with phenol red in acidic medium, at pH 3.0. The analytical parameters have been investigated. The maximum absorbance was obtained at 429 nm and the molar absorptivity of 0.054 L mol-1 cm-1. Beer’s law is linear in the concentration range of 0.5-100 μg/mL for atenolol in Normoten. The detection and quantification limits were found to be 0.038 and 0.113μg/mL for the atenolol in Normoten respectively, and with a linear regression correlation coefficient of 0.997. The recovery was found to be 98.94 to 100.31%. The studied method is can be applied for the determination of atenolol (active ingredient) of the antihypertensive drugs in their pharmaceutical formulations.

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)0.003
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 score0.125
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.000
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 head0.058
GPT teacher head0.442
Teacher spread0.385 · 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