MétaCan
Menu
Back to cohort
Record W1995977846 · doi:10.1155/2013/876939

Effect of Additives on Wood Pellet Physical and Thermal Characteristics: A Review

2013· review· en· W1995977846 on OpenAlex
Dmitry Tarasov, Chander Shahi, Mathew Leitch

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

VenueISRN Forestry · 2013
Typereview
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsLakehead University
Fundersnot available
KeywordsPelletsPelletPulp and paper industryBiomass (ecology)Raw materialHeat of combustionDolomiteMaterials scienceCarbon monoxideWaste managementStarchComposite materialChemistryFood scienceCombustionAgronomyOrganic chemistryMetallurgyCatalysis

Abstract

fetched live from OpenAlex

Additives play a major role in wood pellet characteristics and are a subject of major interest as they act as binding agents for the biomass raw material. Past research has reported the use of lignosulphonate, dolomite, starches, potato flour and peel, and some motor and vegetable oils as additives for wood pellet production. This paper reviews the available research on the effect of different additives on wood pellets' physical and thermal characteristics. It was found that lignosulphonate and starch additives improve the mechanical durability but tend to reduce the calorific value of the wood pellets. Motor and vegetable oil additives increase the calorific value minimally but significantly increase carbon monoxide emissions. Corn starch and dolomite additives also significantly increase carbon monoxide emissions. In order to produce wood pellets with desired physical and thermal characteristics, a suitable additive with the right biomass material should be used.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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