Introduction: Forest Health Monitoring 2008 National Technical Report
Bibliographic record
Abstract
Potter, K.M. 2012. Introduction. Chapter 1 in K.M. Potter and B.L. Conkling, eds., Forest Health Monitoring 2008 National Technical Report. General Technical Report SRS-158. Asheville, North Carolina: U.S. Department of Agriculture, Forest Service, Southern Research Station. pp. 9-19. Healthy ecosystems are those that are stable and sustainable, able to maintain their organization and autonomy over time while remaining resilient to stress (Costanza 1992). The Forest Health Monitoring Program (FHM) of the U.S. Forest Service, with its cooperating researchers within and outside the Forest Service, quantifies the health of U.S. forests within the context of the sustainable forest management criteria and indicators outlined in the Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests (Montréal Process Working Group 2007). The analyses and results outlined in this FHM annual national technical report offer a snapshot of the current condition of U.S. forests from a national or a multi-state regional perspective, incorporating baseline investigations of forest ecosystem health, examination of change over time in forest health metrics, and the assessment of developing threats to forest stability and sustainability. Several chapters also describe new techniques for analyzing forest health data as well as new applications of established techniques. Finally, this report presents results from recently completed evaluation monitoring (EM) projects that have been funded through the FHM national program to determine the extent, severity and/or causes of forest health problems (Forest Health Monitoring 2008).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.071 |
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".