Vegetation dynamics models: a comprehensive set for natural resource assessment and planning in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the context of widespread ecological changes, land managers and policymakers confront the need to prioritize ecosystem restoration and fuel management activities across large areas to sustain ecosystem services. Reference conditions inform prioritization efforts by providing a baseline from which to measure where and how vegetation and fuels have changed, but until recently the USA lacked a complete set of reference conditions. We describe the ongoing development of a comprehensive set of vegetation reference conditions based on over 900 quantitative vegetation dynamic models and accompanying description documents for terrestrial ecosystems in the USA. These models and description documents, collaboratively developed by more than 800 experts around the country through the interagency LANDFIRE Program, synthesize fundamental ecological information about ecosystem dynamics, structure, composition, and disturbance regimes before European‐American settlement. These products establish the first comprehensive national baseline for measuring vegetation change in the USA, providing land managers and policymakers with a tool to support vegetation restoration and fuel management activities at regional to national scales. Users have applied these products to support a variety of land management needs including exploring ecosystem dynamics, assessing current and desired conditions, and simulating the effects of management actions. In an era of rapid ecological change, these products provide land managers with an adaptable tool for understanding ecosystems and predicting possible future conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it