Arriving at a tipping point for worldwide forest decline due to accelerating climatic change
Bibliographic record
Abstract
The 2023–2024 El Niño is inducing an acceleration of global warming that is likely to far exceed 1.5 °C. The Boreal summer of 2023 provided numerous examples of catastrophic forest fires (e.g., >18 million hectares of forest burned in Canada, making the Canadian forest a clear carbon source rather than a carbon sink), a trend that has been accompanied by worldwide examples of unusual tree mortality linked to hotter droughts. It is reasonable to expect that the warming induced by El Niño could push forests in several parts of the world over a tipping point, where they will hardly be able to recover their original state. It is therefore necessary to address the meaning, realistically, of sustainable forest management in the era of accelerated climatic change. The ultimate goal of the broadly accepted silvicultural practice of maintaining forests in a state that resembles what we recognize as temperate or boreal forests is becoming more of an idealistic dream rather than an attainable goal. Thus, the time has arrived to discuss painful forest management decisions, such as anticipated thinning to reduce water competition and the gradual replacement of native local forest populations with more drought-resistant provenances and species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".