Landowner Outreach Education Project Evaluation: Connecting New Family Forest Owners with the Professional Forestry Community
Bibliographic record
Abstract
The majority of West Virginia's forested land is owned by private family forest owners. These individually owned woodlands significantly impact the whole landscape. Connecting with this population and linking them with services and organizations that offer support is essential as these individuals and families establish their ideal woodland. Absentee landowners, land transfers, and increasing industry and development bring to the landscape a high risk for parcelization and fragmentation. With knowledge and forest management information, many woodland owners can improve the overall health, sustainability, and productivity of these lands.;One of the challenges in promoting sustainable forestry is finding ways to connect with these many thousands of landowners. Direct marketing efforts are used to capture the attention of contemporary consumers to advertise diverse products. We used a direct marketing campaign to offer woodland related information to new landowners in three distinct urbanizing zones in West Virginia. We followed the idea of the Ohio Welcome Wagon in this effort.;The West Virginia Woodland Welcome Wagon began reaching out to new woodland owners through a 10 county pilot program. These new landowners were identified through state tax records and contacted via direct mail. An initial mailing, using postcards, was carried out to allow landowners to request a forestry resource information packet as well as be invited to upcoming workshops. This document will address the findings of a follow up survey conducted six months after the initial mailing.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".