Using GIS and landowner survey to determine if the Forest Stewardship Program is effective at generating involvement in forest health issues in West Virginia
Bibliographic record
Abstract
A risk rating map created from 22 shapefiles of pest and disease activity data was used to produce maps of individual properties distributed to half of survey subjects along with a postcard questionnaire. In total, 933 landowners were surveyed; 21% responded. The affects of three factors---the Forest Stewardship Program (FSP), region, and a map---on landowner interest were investigated using ANOVA and logistic regression. The affects of covariables risk rating and acreage were evaluated using ANCOVA. Logistic regression identified preferred delivery methods and pests and diseases relevant to landowners. FSP participation significantly affected interest level, the selection of gypsy moth, and requests for information. Region significantly affected risk rating and the selection of Beech Bark Disease. Map reception did not significantly affect any dependent variable. Acreage and risk rating were insignificant covariables. Sudden Oak Death and information sheets were the most often chosen pests and delivery methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".