Bibliographic record
Abstract
This study analyzes the economics of transshipping biomass from truck to train in a North American setting. Transshipment will only be economic when the cost per unit distance of a second transportation mode is less than the original mode. There is an optimum number of transshipment terminals which is related to biomass yield. Transshipment incurs incremental fixed costs, and hence there is a minimum shipping distance for rail transport above which lower costs/km offset the incremental fixed costs. For transport by dedicated unit train with an optimum number of terminals, the minimum economic rail shipping distance for straw is 170 km, and for boreal forest harvest residue wood chips is 145 km. The minimum economic shipping distance for straw exceeds the biomass draw distance for economically sized centrally located power plants, and hence the prospects for rail transport are limited to cases in which traffic congestion from truck transport would otherwise preclude project development. Ideally, wood chip transport costs would be lowered by rail transshipment for an economically sized centrally located power plant, but in a specific case in Alberta, Canada, the layout of existing rail lines precludes a centrally located plant supplied by rail, whereas a more versatile road system enables it by truck. Hence for wood chips as well as straw the economic incentive for rail transport to centrally located processing plants is limited. Rail transshipment may still be preferred in cases in which road congestion precludes truck delivery, for example as result of community objections.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".