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Record W2587483775 · doi:10.5558/tfc2016-077

Efficient forest fuel supply systems: Research, development and dissemination of knowledge in Sweden

2016· article· en· W2587483775 on OpenAlexvenueno aff
Maria Iwarsson Wide

Bibliographic record

VenueThe Forestry Chronicle · 2016
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDisseminationBusinessAgency (philosophy)Energy sectorOrder (exchange)Environmental resource managementEnvironmental economicsEngineeringFinanceEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

Efficient Forest Fuel Supply Systems (ESS) was run as a collaboration program, financed by the forestry sector, the energy sector, and the Swedish Energy Agency. The objective was to enable a long-term, sustainable and greatly increased use of forest fuel by supporting the development of a more efficient production system. The financial framework of ESS was SEK 130 million (approximately CA $ 19.5 million) over eight years, and the program supported approximately 150 research and development projects. Skogforsk administered the program, and was responsible for coordination and disseminating information to the stakeholders. A program board made formal decisions, and a fuel technology collaboration group helped to identify R&D areas. A project pilot was linked to each project to ensure that the focus was on sector needs and interests, and to help the project manager with relevant study objects, networks and updated information. In and around the program, valuable expertise and networks were built up in each sector and in research organisations, both nationally and internationally. Great emphasis was placed on practical demonstration, implementation and communication, in order to disseminate knowledge about new technology and methods and to influence attitudes toward forest fuel harvest. The forestry sector and its contractors gradually strengthened the supply system through improved skills, better organisation, and advanced equipment. The goals were largely attained, and practical aspects relating to forest fuel were implemented, incorporating many of the results.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.284
Teacher spread0.261 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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