Efficient forest fuel supply systems: Research, development and dissemination of knowledge in Sweden
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".