Old-growth Forests: Anatomy of a Wicked Problem
Bibliographic record
Abstract
Old-growth forest is an often-used term that seems to be intuitively understood by ecologists and forest managers, and the wide-ranging discussion of its social and ecological values suggests it has currency among the general public as well. However, a decades-long discourse regarding a generally acceptable definition of old-growth, in both conceptual and practical terms, has gone largely unresolved. This is partially because old-growth is simultaneously an ecological state, a value-laden social concept, and a polarizing political phenomenon, each facet of its identity influencing the others in complex ways. However, the public, scientific, and management discourse on old-growth has also suffered from simplifying tendencies which are at odds with old-growth’s inherently complex nature. Such complexity confounds simple or rationalistic management approaches, and the forest management arena has witnessed the collision of impassioned and contradictory opinions on the ‘right way’ to manage old-growth forests, ranging from strict preservationism to utilitarian indifference. What is clear is that management approaches that circumvent, trivialize, eliminate, or ignore old-growth’s inherent complexity may do so at the expense of the very characteristics from which old-growth derives its perceived value. We explore the paradoxes presented by the various approaches to old-growth description and definition and present some plausible paths forward for old-growth theory and management, with a particular focus on managed forests.
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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 | 0.000 | 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) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".