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Record W2007209902 · doi:10.1089/ind.2007.3.112

Selecting the most appropriate products for the forest biorefinery

2007· article· en· W2007209902 on OpenAlex
Virginie Chambost, Paul Stuart

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

VenueIndustrial Biotechnology · 2007
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsPolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
FundersNational Renewable Energy Laboratory
KeywordsBiorefineryFlexibility (engineering)Identification (biology)Product (mathematics)Forest productSupply chainProcess (computing)BusinessRisk analysis (engineering)Computer scienceEngineeringEconomicsEnvironmental scienceMarketingForest managementBiofuel

Abstract

fetched live from OpenAlex

The forest biorefinery offers a business strategy that potential forestry companies are seriously considering for improving the overall financial performance of the sector; however, there are considerable technology and business risks related to its implementation. These risks can be mitigated to a great extent by using systematic product and process design tools for analyzing biorefinery strategies. This paper describes the basis for a systematic product design methodology for Rapid Market Analysis, suitable for evaluating the economic and commercial potential of a biorefinery project, using a set of business tools that includes market and synergy identification. The preliminary application of this methodology has illustrated that the longer-term competitive advantage of companies implementing the biorefinery is unlikely to be related to technology, but rather, related to the unique supply chain that companies put in place, coupled with manufacturing flexibility.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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