Editorial: Forests of high naturalness as references for management and conservation: Potential and pitfalls
Bibliographic record
Abstract
Forest ecosystems are critical to address the collapse of biodiversity and climate change crisis faced by our societies.The many habitats and ecosystem services they provide, such as carbon sequestration or water cycle regulation, need to be protected.In this context, forests of high naturalness have an exceptional importance because of the higher amount and quality of ecosystem services they provide compared to managed forests (Watson et al., 2018)."Naturalness" describes a gradient of human impact on nature, with naturalness increasing as human impact decreases (Winter, 2012).This concept is however rooted in the Western paradigm of "nature/culture" distinction (Ducarme et al., 2021).In this Research Topic, Clement et al. emphasize that high naturalness does not mean an absence of humans and interactions with their environment.The authors focus on the Amazonian Indigenous Peoples, who have been using and changing the Amazonian forest for millennia, a forest that is at the same time recognized for its high naturalness value (Potapov et al., 2008;Venter et al., 2016).Acknowledging that the loss of naturalness is mainly due to modern industrial activities, and not to human presence per se, is essential for considering the various issues explored in this Research Topic.For example, the Food and Agriculture Organization of the United Nations has recently recognized that a forest showing evidences of traditional indigenous activities can still be considered a primary forest (FAO, 2020).Reducing or even halting the degradation of forests of high naturalness is a critical issue, as modern human activities continue to cause their loss around the world (Potapov et al., 2017), aggravating climate change and biodiversity loss crises.Tropical and boreal forests contain the largest remaining area of forests
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".