Bibliographic record
Abstract
The Forest Health Monitoring Program produces an annual technical report that has two main objectives. The first objective is to present information about forest health from a national perspective. The second objective is to present examples of useful techniques for analyzing forest health data new to the annual national reports and new applications of techniques formerly used. The report’s organizational framework is the Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests of the Montreal Process. Here, we present an approach to examining landscape context of forest and grassland in the United States. We explore the influence of environmental factors such as climate and air quality on a lichen species diversity indicator across the continental United States. This includes an analysis of the potential for monitoring changes in these environmental factors. We use Forest Inventory and Analysis phase 3 data to describe aspects of forest communities such as understory species composition, richness, and distribution, including discussion of invasive and introduced species. Tree mortality, which has been examined in previous Forest Health Monitoring reports, is analyzed in this report using a more intensive dataset to demonstrate the utility of Forest Inventory and Analysis phase 2 data. We explore spatial modeling of ozone injury risk, along with microscale and landscape-scale ancillary data that can be used in the modeling analyses. A discussion of redbay ambrosia beetle/laurel wilt risk includes current beetle/ wilt distribution, host species distributions, climate matching, and spread modeling. Progress in monitoring and analyses of Phytophthora ramorum and sudden oak death is presented along with results from two different monitoring techniques.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".