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Record W4242572330 · doi:10.24124/2009/bpgub1385

Challenges and opportunities for the production of ethanol from MPB killed biomass in Interior BC

2009· dissertation· en· W4242572330 on OpenAlexaff
Stuart W. Sinclair

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversity of AlbertaUniversity of Northern British Columbia
Fundersnot available
KeywordsBiofuelBiomass (ecology)Production (economics)BusinessNatural resource economicsRaw materialGovernment (linguistics)Agricultural economicsEconomicsEngineeringWaste managementEcology

Abstract

fetched live from OpenAlex

The short-term over supply of biomass feedstock in the Interior of British Columbia resulting from the Mountain Pine Beetle (MPB) infestation has created a mix of challenges and opportunities for the development of a biofuel industry. Several challenges include: the current economic downturn , difficulty in obtaining financing , insufficient government support, lack of security of long term biomass supply, and technological limitations. There are also several opportunities, which need to be maximized in order for this biofuel industry to succeed. They include: the growing market demand for biofuel , new approaches to biomass utilization , high value coproducts, inter-industry synergies and utilizing short-term availability of beetle killed wood , which offers cost and time savings as well as higher yield capacity. This project was designed to identify the opportunities for, and the barriers for biofuel production, specifically ethanol, to determine if it would be a potential component to a successful rural economic development plan , while at the same time providing additional salvage opportunities for dead pine stands in the Interior of BC. The results indicate that the dead pine biomass does provide significant short-term opportunities, however persistent technological barriers , uncertain economic times and long term fibre supply issues still present barriers that need to be overcome .

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.437

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 head0.054
GPT teacher head0.262
Teacher spread0.208 · 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 designOther design
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

Citations1
Published2009
Admission routes1
Has abstractyes

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