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Record W2110150833 · doi:10.1002/ente.201402001

Rheological, Thermal, and Physicochemical Characterization of Animal Fat Wastes for use in Biodiesel Production

2014· article· en· W2110150833 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsMcGill University
FundersMcGill University
KeywordsTransesterificationRheologyGreaseAnimal fatTallowBiodiesel productionBiodieselMaterials scienceFood scienceChemical engineeringChemistryOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Abstract The rheological, thermal and physicochemical properties of animal fat wastes (tallow, lard, choice white grease, and yellow grease) are important parameters for an efficient design of equipment and to optimize the processing procedures of biodiesel production. In this study, the physicochemical properties of animal fat waste samples and the correlation of these properties to their thermal and rheological behaviors were elucidated. Pure lard was equally investigated to monitor the effects of impurities on the rheological and thermal properties of the animal fat wastes. It was established that the presence of impurities had effects on the rheological and thermal properties of fats. Additionally, due to the high level of free fatty acid (FFA) present in the wastes, transesterification cannot be applied directly. It will be necessary to reduce the FFA level by using acid pretreatment or enzyme catalyzed transesterification.

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 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 score0.022
Threshold uncertainty score0.281

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 integrity0.0000.000
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 head0.013
GPT teacher head0.203
Teacher spread0.191 · 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