MétaCan
Menu
Back to cohort
Record W2086441352 · doi:10.1080/10942912.2010.492543

Characterization of The Physical Properties of Palm Kernel Cake

2011· article· en· W2086441352 on OpenAlexaff
Horng Yuan Saw, Jidon Janaun, S. Kumaresan, C. M. Chu

Bibliographic record

VenueInternational Journal of Food Properties · 2011
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of British Columbia
FundersUniversiti Malaysia Sabah
KeywordsPalm kernelAgglomerateMaterials scienceParticle sizeParticle densityPorosityParticle (ecology)Bulk densityScanning electron microscopeEconomies of agglomerationVolume (thermodynamics)Kernel (algebra)Composite materialMineralogyMathematicsChemistryChemical engineeringFood sciencePalm oilPhysicsSoil scienceThermodynamics

Abstract

fetched live from OpenAlex

A systematic sieving method (1 kg sample; 50 Hz; 0° inclination; 20 min) was used to obtain particle size distribution of palm kernel cake containing seven different particle sizes (4.83, 2.68, 1.50, 0.80, 0.51, 0.32, and 0.11 mm). Regardless of particle size, palm kernel cake was found to be of different shapes qualitatively with optical microscopy and quantitatively (variation in mean length, mean volume, and volume-surface mean diameters), non-porous (Brunauer-Emmett-Teller specific surface area <1 m2/g), and to contain an uneven rough surface, as shown in scanning electron microscopy. Palm kernel cake of 0.32 mm and less were aggregates with uneven rough surface, and those of 0.51 mm and more were agglomerates with interstices formed from particle agglomeration. These characteristics affected the bulk density of palm kernel cake that decreased with decreasing particle sizes due to lower packing density and higher void. The physical properties affected the hydration properties. This information is useful for the solid-state fermentation of palm kernel cake.

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.

How this classification was reachedexpand

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 score0.014
Threshold uncertainty score0.304

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 science0.0010.000
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 head0.054
GPT teacher head0.239
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Food PropertiesSame topicFood composition and propertiesFrench-language works237,207