Small differences in the chemistry of tropical trees have big impacts on climate change modeling
Bibliographic record
Abstract
Tropical forests are some of the most diverse and beautiful places in the world; they also represent some of the last stretches of undisturbed or “frontier” ecosystems on the planet. In addition to playing host to some of Earth’s most amazing and unique plants, animals and insects, tropical forests also play a key role in regulating the planet’s climate. That’s because across the globe, the enormous trees in tropical forests store up to 40-50% of the world’s land-based carbon1. (The oceans also store a massive amount of the world’s carbon, but we’re not taking that into account here). While this is good news for the Earth’s climate, it also means that any human activities that destroy or degrade tropical forests can have an extremely large impact on climate change. Currently it’s estimated that humans release roughly 9.2 gigatonnes of carbon per year on average2. This amount is so large it is difficult to understand what it means, but this is about equivalent to releasing the weight of 9.2 trillion full-grown cows worth of carbon into the atmosphere, mostly as carbon dioxide gas. Much of this carbon is released into the atmosphere when we use fossil fuels in our vehicles, produce agricultural products, or cut down Earth’s forests. Over the past 250 years these activities have led (and will continue to lead) to considerable changes in Earth’s climate including warmer air and ocean temperatures, droughts, melting glaciers and rising sea levels2. Of humans’ total carbon emissions in recent years from the 1990s through the 2000s, about 12-20% comes from the destruction or degradation of tropical forests – when trees are cut down, the carbon locked up in trees is slowly released into the atmosphere as the wood decays3. This is why measuring and mapping carbon (or performing “carbon accounting”) in tropical forests throughout the world is a critical part of climate change science.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".