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Record W2794366271 · doi:10.3390/en11030648

Small Landowner Production of Pellets from Green, Beetle-Killed, and Burned Lodgepole Pine

2018· article· en· W2794366271 on OpenAlexaboutno aff
Xuexian Qin, Robert Keefe, Daren E. Daugaard

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsPelletsRaw materialBioenergyEnvironmental sciencePinus contortaPelletBark beetlePulp and paper industryMountain pine beetleBark (sound)AgroforestryBiofuelForestryWaste managementGeographyMaterials scienceEngineeringEcology

Abstract

fetched live from OpenAlex

To meet the growing need for raw materials to produce pellets and wood-based biofuels, trees killed by natural disturbances have increasingly been considered as potential feedstock in bioenergy development scenarios in the Western U.S. and Canada. While much research has focused on utilization of beetle-killed and fire-salvaged timber from federal lands in this region, small private landowners make up a large portion of land holdings in the Rocky Mountain Region and may also provide an important potential supply of uniform feedstock pellets in decentralized energy supply systems in the future. In this paper, we evaluated the quality of pellets produced from green, beetle-killed, and burned lodgepole pine with and without bark using a chipper, hammer mill, and pellet mill intended for use by small landowners. Results show that green, beetle-killed, and fire-salvaged lodgepole pine produced by small landowners, including material with bark, are suitable as feedstock for pellet production. Further, pellet quality can be varied through the blending of source lodgepole pine products when needed to meet pellet quality standards.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2018
Admission routes1
Has abstractyes

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