Internet-Based Forestry Extension: Using It in the Classroom
Bibliographic record
Abstract
The Internet Forestry Explorer is a Web site designed to present information about forest and watershed management. The target audience includes natural resource professionals, forest landowners, environmental education teachers, citizens of the featured forests and watersheds, and to a lesser extent anyone who is interested in learning more about their state’s natural resources. Components of the Web site include an interactive GIS that allows the creation of tailored maps, virtual “walking tours” where users can view photos, pages of forest management examples, and links to organizations and other pages of interest. One component was created as part of a three-university virtual forest project. Penn State chose a private, award-winning tree farm to discuss and highlight forest sustainability according to the Montreal Protocol. This site includes pages describing the 7 criteria and 67 indicators of the Montreal Protocol, as well as a walking tour of the tree farm. A survey was conducted to analyze the effectiveness of the Internet Forestry Explorer in educating target audiences. This presentation will discuss the technical construction of the Web site, the results of the survey, and how the site may be used in university classroom setting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".