Wood supply chain efficiency and fiber cost what can we do better
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fiber is the largest component of cash manufacturing costs. As such, fiber availability and cost have large impacts on industrial profitability. We begin with the examination of wood supply chains across the world’s major wood producing regions, including the U.S. South, Western Canada, Brazil, Sweden, and Australia. We evaluate the effectiveness of particular systems based on information about their structure, stumpage costs, and delivered wood costs. Using the linerboard sector as an example, we also examine the impact of using virgin fiber vs. recycled fiber on manufacturing costs. These regional comparisons are used to identify strategies that should be considered by the industry in the U.S. South for improving wood supply chain efficiency. A special emphasis is placed on what wood processing mills can do to improve the wood supply chain efficiency, both in terms of reducing costs and improving fiber availability, including policies associated with truck weight limits, scheduling, equipment, and contracting.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it