Bibliographic record
Abstract
Non-timber forest products (NTFPs) have long been collected for food, medicine, income, and pleasure, traditionally in rural areas. NTFPs include edible products (mushrooms, nuts, berries, etc), specialty wood products (hand carvings, walking canes, etc.), floral and decorative products (moss, vines, etc.), and medicinal products (ginseng, goldenseal, black cohosh, etc.) and reported sales contribute over {dollar}30 million each year to the West Virginia economy.;West Virginia landowners possess some of the most biologically diverse forest in the United States. Many plants and other products can be derived from these forests for social and economic value. In order to educate, arouse curiosity, and stimulate interest, a variety of outreach methods were employed to best inform landowners about NTFPs One method was to construct six demonstration areas (one in each of the former six Division of Forestry districts) showcasing the most commonly harvested medicinal plants. An "inoculate-your-own" shiitake mushroom workshop was also conducted at each of the sites. The second method used was to compose a booklet of ten case studies that highlighted the challenges/successes, production methods, organizational structure, and marketing methods used by landowners who started and succeeded with their own forestbased business. Finally, in an attempt to gain knowledge about the existing harvesting and interest levels among landowners, a survey was conducted that targeted four counties, two from the traditional blue-collared western region and two from the rapidly urbanizing counties in the eastern panhandle. General demographic information (age, gender, income, education, and occupation) was also collected. In addition, a landowner's willingness to pay and travel to a two-hour workshop was collected to determine future interest and workshop locations regarding NTFPs.;The survey yielded a 32% response rate (531 valid responses; 1649 surveys mailed). Landowners who are from the western region (rural) were 2.7 times (p=0.0105, chi 6.55) more likely to harvest medicinal herbs than 'urban' landowners. The size of the respondent landholdings (in acres) was a significant variable in the medicinal herb, edible, and specialty wood product categories, while age was significant in all four categories. The survey also indicated that landowners who own forested land are also more willing to pay and travel to a two hour workshop than those who do not own forested land.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".