Bibliographic record
Abstract
Fossil fuels such as oil, coal and gas dominate the global energy supply, covering more than 80 % of the total primary energy supply of 508 EJ in 2009 [1]. In order to reach climate targets and create low-carbon economies, biomass is expected to play a pivotal role. While the future resource potential of biomass may be significant and the global trade of bioenergy is rapidly expanding, biomass is currently only playing a minor role in the global energy supply. Total biomass primary energy supply was 51 EJ in 2008, of which more than 60 % constituted for traditional use such as cooking and heating in developing countries (India and sub-Saharan Africa) [1]. The main applications of modern use of biomass are today firstly, in the industrial sector to produce process steam, and secondly, in the power sector. Major drivers for the growth of bioenergy are the large resources potential and low production costs of biomass in export countries such as Brazil and Canada, high fossil fuel prices, and a variety of policy incentives to stimulate biomass use in import countries. Recent evaluations of the biomass resource potential show that biomass could in a sustainable manner contribute with as much as 160-270 EJ to the world’s primary energy supply by 2050 [2]. However, a number of barriers need to be overcome before such a potential could be realized. One of the main obstacles is that large biomass
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.453 | 0.306 |
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".