Bibliographic record
Abstract
COULSTON, JOHN WESLEY. Large-scale analysis of sustainable forest management indicators: assessments of air pollution, forest disturbance, and biodiversity (Under the direction of Heather Cheshire) As the doubling time of the global human population decreases, increasing emphasis is placed on sustainable development by both policy makers and scientists. Sustainable forest management is one part of the overall picture of sustainable development. One method to assess sustainable forest management is through the use of criteria and indicators. Criteria represent sustainable management goals. Indicators are measurable quantities that designate whether the goals are being met. The maintenance of forest health and vitality is a criterion of the Montréal Process Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests. Measures of air pollution, forest disturbance, and change in ecological integrity provide indicators of how well forest health and vitality are being maintained. Using national databases, I assess air pollution in the United States, demonstrate the use of epidemiological approaches to examine forest disturbances, and develop an analytical technique to identify gaps and target priorities in reserve networks.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.348 | 0.180 |
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".