NINE WAYS TO AVOID THE AMAZON TIPPING POINT
Bibliographic record
Abstract
Global greenhouse gas emissions, combined with local deforestation and forest degradation, are pushing the Amazonian system closer to a tipping point. A large-scale Amazon tipping point may trigger the collapse of most forests and consequently: (1) accelerate global warming, hindering efforts to achieve the goals of the Paris Agreement; (2) reduce moisture flow across South America, threatening water security for basic socioeconomic activities, such as agriculture; (3) increase temperatures across the Amazon region that may become unbearable for humans living in urban and rural areas; (4) cause mass species extinctions; and (5) compromise the biological and cultural assets that represent key solutions to the current and future challenges of humanity. Synergies between disturbances may cause unexpected tipping behaviour, even in forest regions previously considered as resilient to climate change, such as the central or western Amazon.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.009 |
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; both teacher heads agree on what is shown here.
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".