Developing an economic model for transporting wood ash for beneficial uses
Bibliographic record
Abstract
A technically feasible wood ash application can fail due to poor economics stemming from lower ash value and high hauling and spreading costs. Therefore, this study aims to develop an economic model for ash hauling through cost-benefit analysis, estimating feasible transport distances. The model assigns hauling expenses as cost, while ash dollar value and landfill cost savings, as benefits. We evaluated factors, such as truck driver rates, truck volume and speed, payload capacity, and loading/unloading times. The calculated ash value was $58/t, with costs for spreading, landfill savings, truck size, and backhauling affecting hauling distances. The model suggests economic round-trip distances of 300 km with backhauls, 170 km without them, and about 65 km when spreading was necessary. Overall, it shows that avoiding spreading and using backhauls results in the longest economic hauling distance. This model will help ash producers and users determine optimal hauling distances for ash valorisation projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".