Ecological Silviculture for Aspen Mixedwoods in Western Canada
Bibliographic record
Abstract
The subject of our proposed book is broadly ecological silviculture, which is a transformative approach for managing forests ecosystems based on emulation of natural models of development and that explicitly incorporates ecological objectives into prescriptions for on-the-ground activities. Specifically, our proposal is for a book that presents examples of comprehensive forest developmental models and corresponding ecological silvicultural systems for major temperate and boreal forests in North America, as well as select forest ecosystems in Europe, South American, and Australasia. These examples will be presented in contributed chapters written by innovators in the field. We are pioneers in developing the fundamental concepts of ecological silviculture and translating concepts into principles and guidelines for on-the-ground management, having already authored the first of its kind textbook on the subject (Palik et al. 2020, Waveland Press), as well as several other influential papers that addresses various aspects of ecological silviculture (e.g., Franklin, Mitchell, and Palik 2007; D'Amato et al. 2017; Palik and D'Amato 2017; D'Amato and Palik 2021). In our textbook (Palik et al. 2020), we were only able to highlight four examples of ecological silviculture systems, all from the United States and focused on only a limited suite of ecosystems. We know there is wealth of additional exemplary approaches to ecological silviculture that can be highlighted in our proposed book, making the knowledge and experience of innovators in the field readily available to a global population of interested stakeholders
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".