Out with <scp>OLD</scp> growth, in with ecological contin<scp>NEW</scp>ity: new perspectives on forest conservation
Bibliographic record
Abstract
Forest managers have a responsibility to identify and conserve ecologically exceptional forest stands. In North America, priority areas of old‐growth forest are often identified based primarily on the age of trees within the stand. However, delineating forests with high conservation value based solely on tree age is an oversimplification. Therefore, we propose a different view – that of forest continuity, a view that is more prevalent in Europe. We contend that forests that have been continuously wooded over time, whether old‐growth trees are present or not, have higher conservation value than areas that have old trees but that may not always have been forested. Identifying forests with high continuity requires a different index than tree age. We argue that the relative richness and abundance of lichens can be effective indicators of forest continuity, discuss how forest managers might operationalize this system, and explain why it might be a more ecologically relevant indicator of priority forest areas.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".