Bibliographic record
Abstract
The U.S. production of wood pellets has grown to over 8 million tons by 2015 (FAO 2016). The majority of the production capacity increase took place in the U.S. Southeast and is associated with industrial wood pellet production for export markets. In 2015, over 5 million tons were exported, 99% of which went to EU markets (84% to the UK alone) (EUROSTAT 2015, FAOSTAT 2016, USDA 2016). The production and consumption growth of U.S. residential wood pellets has been slow in comparison. The industrial and residential wood pellet markets are beginning to merge slowly, but production excess (e.g., in 2015 and 2016) in the industrial wood pellet segment could not be fully absorbed by domestic or oversea residential markets. The main growth path for U.S. wood pellet production is still seen to lie in oversea markets (Bingham 2016, Strauss 2016). Demand increases for industrial pellets are projected to occur within the next years in Europe (Netherlands, Denmark, UK, Belgium) and Asia (predominantly Japan). At the same time, subsidy schemes in Europe are also bound to be phased out within the next decade. U.S. suppliers will have to compete with other supply regions (e.g., Western Canada to Asia) in the long-term, and are already focusing on selling more and more into non-subsidized markets such as biochemicals, absorbents, and soil amendments (Keppler 2016). An outlook of U.S. industrial wood pellet production under a potential growth in U.S. biopower demand and a lack thereof shows a wood pellet oversupply of 9-10 million tons by 2030, equaling roughly 12.5 million tons of production capacity, 20-30 large-scale plants, or 15 million green tons of woody biomass. Once the U.S. production of wood pellets outgrows combustion market demand (which will plateau even with a domestic expansion of biopower) the industry will require additional outlets to avoid a structural decline and bankruptcy. This will create a resource push and cost reduction, driving the development of a U.S. cellulosic biofuels industry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.031 |
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".