MétaCan
Menu
Back to cohort
Record W2012906409 · doi:10.1021/ef101683s

Energy Input and Quality of Pellets Made from Steam-Exploded Douglas Fir (Pseudotsuga menziesii)

2011· article· en· W2012906409 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

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPelletsPelletizingSoftwoodSteam explosionWater contentMaterials scienceDouglas firComposite materialEquilibrium moisture contentPelletPulp and paper industryMoistureWaste managementChemistrySorptionBotanyAdsorption

Abstract

fetched live from OpenAlex

Ground softwood Douglas fir ( Pseudotsuga menziesii ) was treated with pressurized saturated steam at 200−220 °C (1.6−2.4 MPa) for 5−10 min in a sealed container. The contents of the container were released to the atmosphere for a sudden decompression. The steam-exploded wood particles were dried to 10% moisture content and pelletized in a single-piston−cylinder system. The pellets were characterized for their mechanical strength, chemical composition, and moisture sorption. The steam-treated wood required 12−81% more energy to compact into pellets than the untreated wood. Pellets made from steam-treated wood had a breaking strength 1.4−3.3 times the strength of pellets made from untreated wood. Steam-treated pellets had a reduced equilibrium moisture content of 2−4% and a reduced expansion after pelletization. There was a slight increase in the high heating value from 18.94 to 20.09 MJ/kg for the treated samples. Steam-treated pellets exhibited a higher lengthwise rigidity compared to untreated pellets.

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 score0.121
Threshold uncertainty score0.876

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 head0.043
GPT teacher head0.210
Teacher spread0.167 · 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