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Record W1970377803 · doi:10.5558/tfc87054-1

What potential customers are telling us: Organizational buyer attitudes towards forest biomass

2011· article· en· W1970377803 on OpenAlexafffundvenueabout
John Nadeau, Kate Griese

Bibliographic record

VenueThe Forestry Chronicle · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsNOSM UniversityNipissing University
FundersGovernment of OntarioNipissing University
KeywordsBiomass (ecology)BusinessFossil fuelKey (lock)MarketingEnvironmental economicsSustainable energyEnergy (signal processing)Renewable energyEconomicsWaste managementEngineeringComputer scienceEcology

Abstract

fetched live from OpenAlex

This paper reports on a study of organizational buyer attitudes towards forest biomass energy for use in heating systems.This topic warrants discussion as global energy needs grow and the Canadian forestry sector experiences economic challenges.In particular, heating systems are an appropriate introductory application for solid forest biomass because it representsan efficient and sustainable fuel use. The attitudes of organizational buyers are assessed and compared against theperceived level of importance for attitudinal items and the views held toward fossil fuels. The results demonstrate that forestbiomass is viewed favourably on environmental aspects and on some other attitudinal items of high importance. Managerialsuggestions are forwarded to guide the burgeoning sector in its attempt to build awareness and strengthen its perceivedimage among organizational buyers. Key words: biomass, biomass fuel, attitudes of organizational buyers, biomass as an alternative heat and energy source

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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 designQualitative
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

Citations0
Published2011
Admission routes4
Has abstractyes

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