The West Virginia Friends of Firewood Network: Engaging with and exploring the practices of firewood producers
Bibliographic record
Abstract
Firewood is the oldest source of energy for cooking and home heating and remains a primary heating source for half of the world's population, yet the industry remains relatively undocumented and immature both in developing and technologically advanced countries. Firewood is an environmentally friendly and renewable resource that is becoming popular once again in U.S. homes, 2.5 million of which utilize it as a primary heating source. However, firewood is also a proven vector for invasive insects and pathogens that are threatening the health of U.S. forests. In order to address the increasing trend of firewood use and prevent further spread of these invasive species, we need to ensure that firewood producers, who are among the first individuals to lay their hands on this raw wood product, are fully educated and making the right decisions regarding the harvest, transport and sale of firewood. Therefore, the objectives of this project were to create a database of West Virginia firewood producers and conduct a statewide two-part survey in order to learn more about their business, safety and wood hygiene practices. It was found that 51% have been in business less than five years, and 56% entered the industry because they had some form of wood residue to dispose of; another 32% saw the opportunity for supplemental income. Seventy-six percent view firewood production as a hobby, but 50% are willing to attend classes in order to attain a Community Firewood Dealer certification. The average length of seasoning is 8.4 months and the average delivery radius of respondents is 29.6 miles, although 21% travel further than 50 miles to deliver. This has implications for the movement of invasive species.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".