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Record W2889923055 · doi:10.1093/jofore/fvy042

Evaluation of Three Forest-Based Bioenergy Development Strategies in the Inland Northwest, United States

2018· article· en· W2889923055 on OpenAlexaff
Darin Saul, Soren Newman, Steven Peterson, Eli Kosse, Ryan Jacobson, Robert Keefe, Stephen Devadoss, Tammy Laninga, Jill Moroney

Bibliographic record

VenueJournal of Forestry · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBioenergyBiomass (ecology)Environmental scienceProfitability indexAgroforestryBiofuelBusinessAgricultural economicsProduction (economics)StakeholderRenewable energyNatural resource economicsEnvironmental protectionEngineeringEconomicsEcologyWaste managementFinance

Abstract

fetched live from OpenAlex

In this article, we compare three bioenergy scenarios that use woody biomass from US Inland Northwest forests. The scenarios are based on current bioenergy research, development efforts, and stakeholder input. They include a small-scale system that produces drop-in transportation biofuel and biochar, a large, regional system that produces bio-aviation fuel, and a midsized pellet production system. We modeled woody biomass harvest, processing, and transportation, and then evaluated profitability and potential socioeconomic impacts to determine the overall viability of each strategy. Through interviews, we found widespread stakeholder support for all three scenarios. Wood-pellet production was profitable and feasible with current prices and conditions, whereas liquid biofuel production was profitable only at levels that greatly exceed current prices.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.333
Teacher spread0.285 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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