Building on the last “new” thing: exploring the compatibility of ecological and adaptation silviculture
Bibliographic record
Abstract
Sustaining the structure, function, and services provided by forest ecosystems in the face of changing climate and disturbance regimes represents a grand challenge for forest managers and policy makers. To address this challenge, a range of adaptation approaches have been proposed centered on conferring ecosystem resilience and adaptive capacity; however, considerable uncertainty exists regarding how to translate these broad and often theoretical adaptation frameworks to on-the-ground practice. Complicating this issue has been movement away, in some cases, from other recent advances in forest management, namely ecological silviculture strategies that often focus on restoration. In this paper, we highlight the areas of compatibility and conflict between these two frameworks by reviewing the four principles of ecological silviculture (continuity, complexity and diversity, timing, and context) from the perspective of global change adaptation. We conclude that given many commonalities between the outcomes of ecological silviculture and conditions conferring adaptive capacity, the four principles remain a relevant starting point for guiding operationalization of often theoretical adaptation strategies.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".