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Moisture sorption isotherms and heat of sorption of Algerian bay leaves (Laurus nobilis)

2015· article· es· W2262111192 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

VenueMaderas Ciencia y tecnología · 2015
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSorptionGravimetric analysisEquilibrium moisture contentMoistureThermodynamicsWater contentChemistryLaurus nobilisWork (physics)ChromatographyAdsorptionOrganic chemistryGeologyPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

The moisture sorption isotherms of Algerian bay leaves (Laurus nobilis) were determined experimentally in this work. The equilibrium moisture contents of the leaves were measured at 40, 50, and 60 °C using static gravimetric method. Six mathematical models were tested to fit the experimental data of sorption isotherms and predict the hygroscopic behavior during storage or drying. Peleg model was found to be the best fitting model for describing the sorption curves. The net isosteric heat of sorption was computed from the equilibrium data at different temperatures by applying the integrated form of the Clausius-Clapeyron equation. The net isosteric heat of sorption is inversely proportional to the equilibrium moisture content and is found to be an exponential function of moisture content.

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 score0.616
Threshold uncertainty score0.540

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 studies0.0000.001
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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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