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Record W2590859123 · doi:10.15761/gimci.1000133

Water activity as related to microorganisms in the manufacturing environment

2016· article· en· W2590859123 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

VenueGeneral Internal Medicine and Clinical Innovations · 2016
Typearticle
Languageen
FieldEngineering
TopicIndustrial Automation and Control Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicroorganismEnvironmental scienceBusinessBiologyBacteria

Abstract

fetched live from OpenAlex

Manufacturing of solid oral dosage formulations is by definition non-sterile. This is in contrast to parenterals which are by definition sterile upon completion of manufacturing. Water is the solvent in which proteins, polysaccharide and lipids are dissolved, but in the confines of solid oral manufacturing may be considered little more than a contaminant. Water may be taken into excipients, which comprise an individual part of the pill or capsule, but which are inert in terms of physiological function. The best, or most noticeable example of an excipient is the gelatin capsule. Gelatin is highly hydroscopic, and attracts water absorbed from the ambient environment. Capsules and pills are normally manufactured in the absence of any water thus limiting the presence of the molecule. The availability of water is the most important single factor affecting the growth of all prokaryotic and eukaryotic cells. The availability of water for a cell depends upon its presence in the atmosphere (relative humidity) or its presence in solution, or a substance. Water activity is affected by the presence of solutes such as salts and/or sugars that may be dissolved in a given solution. The lower the solute concentration of a substance, the higher the water activity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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