Old-growth forest: An ancient and stable sylvan equilibrium, or a relatively transitory ecosystem condition that offers people a visual and emotional feast? Answerit depends
Bibliographic record
Abstract
As a species, humans depend heavily on their visual sense, make decisions as much from their hearts as from their heads (emotion-and value-based decisions versus analytical, logic- and knowledge-based decisions), and dislike environmental and other change. Societies in early stages of development have generally revered old people for their wisdom and experience, whereas many societies at more advanced stages of development have adopted a culture of youth. Attitudes toward forests have shown a similar trend. Respect for large and old trees was a feature of some early societies, whereas societies in and after the industrial revolution became more interested in younger, faster growing trees for technical and utilitarian reasons. However, as human population growth caused the area of unmanaged forest, old forest, and forests of large trees to decline, reverence has revived for large, old trees and for old forests. This trend has not been matched by a renewed respect for scientific knowledge about forests and for wisdom about forests based on long experience. Reflecting the pervasive effects of the culture of youth, issues in forestry, including the issue of old forests, are being judged largely on an aesthetic basis, on human emotional response to snapshot visual aspects, and on a dislike for changethe Peter Pan syndrome. "Old-growth" forest, whatever it is, has been deified as a symbol of the mythical "balance of nature," a concept discredited by ecologists as a Victorian anachronism. There are important spiritual, aesthetic, wildlife, and environmental values associated with old forests, and the area of such forests is declining. There are many valid reasons (social, scientific, and environmental) for sustaining significant and representative areas of such forests. However, conservation of such forests and ensuring a future supply of the values they provide will not be achieved unless the reverential respect for such forests is matched by another meaning of respect: understanding such forests and basing our relationship with them on that understanding. This paper challenges forest managers and forest scientists to gain a significant understanding of "old growth" to provide a logical, knowledge-based, and experience-based foundation for the identification, inventory, conservation, and management of this forest ecosystem condition, and to assert this understanding as a counterbalance to the necessary, but insufficient, value-based attitude toward old forests that arises largely from visual snapshots and the emotions they arouse. Key words: old growth, biodiversity, sustainability, stability, succession, stand dynamics, respect for nature
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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 | 0.001 | 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 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".