Bibliographic record
Abstract
The cost of transporting wood chips by truck and by pipeline as a water slurry was determined. In a practical application of field delivery by truck of biomass to a pipeline inlet, the pipeline will only be economical at large capacity (>0.5 million dry t/yr for a one-way pipeline, and >1.25 million dry t/yr for a two-way pipeline that returns the carrier fluid to the pipeline inlet), and at medium to long distances (>75 km [one-way] and >470 km [two-way] at a capacity of 2 million dry t/yr). Mixed hardwood and softwood chips in western Canada rise in moisture level from about 50% to 67% when transported in water; the loss in lower heating value (LHV) would preclude the use of water slurry pipelines for direct combustion applications. The same chips, when transported in a heavy gas oil, take up as much as 50% oil by weight and result in a fuel that is >30% oil on mass basis and is about twothirds oil on a thermal basis. Uptake of water by straw during slurry transport is so extreme that it has effectively no LHV. Pipeline-delivered biomass could be used in processes that do not produce contained water as a vapor, such as supercritical water gasification.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.016 |
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".