Bibliographic record
Abstract
In our fast-paced world, where almost any destination is a commercial flight away, it is easy to lose a sense of the incremental changes to our habitats. Aitken asks us to consider trees as the bell-weather of climate change. Her overview starts in the boreal forests of Canada, where warming temperatures have had a doubly negative effect on trees through the spread of insects, whose damage was previously kept in check by colder annual temperatures. In turn, these habitats have also witnessed the devastating impact of wildfires. Globally, the prospects are yet more chilling. In California alone, over 150 million trees have been lost in recent years due to consistent seasons of drought. The climate-induced effects on trees perhaps pales in comparison to the indiscriminate global destruction of forests by humans. Tropical forests have suffered in particular as the demand for meat and cheap palm oil used in processed foods has devastated vast swathes of the Amazonian and southeast Asian rainforests, now permanently lost to cattle ranches and monoculture plantations. Aitken notes throughout that the recently fashionable idea of planting trees is no magic wand. Planting trees globally cannot simply be a like-for-like replacement for the vast, intricately connected and unimaginably biodiverse ecosystems represented by the world’s forests. Instead we must focus on the absolute necessity of halting current rates of deforestation and finding sustainable ways of managing forest habitats, and the lives of communities that depend on them. Looking further, the future of humanity depends on the hugely complex, and seemingly insurmountable, task of remodeling our global energy, production and transportation networks. We must begin now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.238 | 0.139 |
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".