Time to treat the climate and nature crisis as one indivisible global health emergency
Bibliographic record
Abstract
Time to treat the climate and nature crisis as one indivisible global health emergencyOver 200 health journals call on the United Nations, political leaders, and health professionals to recognise that climate change and biodiversity loss are one indivisible crisis and must be tackled together to preserve health and avoid catastrophe.This overall environmental crisis is now so severe as to be a global health emergency.The world is currently responding to the climate crisis and the nature crisis as if they were separate challenges.This is a dangerous mistake.The 28 th Conference of the Parties (COP) on climate change is about to be held in Dubai while the 16 th COP on biodiversity is due to be held in Turkey in 2024.The research communities that provide the evidence for the two COPs are unfortunately largely separate, but they were brought together for a workshop in 2020 when they concluded that: "Only by considering climate and biodiversity as parts of the same complex problem…can solutions be developed that avoid maladaptation and maximize the beneficial outcomes."[1] As the health world has recognised with the development of the concept of planetary health, the natural world is made up of one overall interdependent system.Damage to one subsystem can create feedback that damages another--for example, drought, wildfires, floods and the other effects of rising global temperatures destroy plant life, and lead to soil erosion and so inhibit carbon storage, which means more global warming.[2] Climate change is set to overtake deforestation and other land-use change as the primary driver of nature loss.[3] Nature has a remarkable power to restore.For example, deforested land can revert to forest through natural regeneration, and marine phytoplankton, which act as natural carbon stores, turn over one billion tonnes of photosynthesising biomass every eight days.[4] Indigenous land and sea management has a particularly important role to play in regeneration and continuing care.[5] Restoring one subsystem can help another--for example, replenishing soil could help remove greenhouse gases from the atmosphere on a vast scale.[6] But actions that may benefit one subsystem can harm another--for example, planting forests with one type of tree can remove carbon dioxide from the air but can damage the biodiversity that is fundamental to healthy ecosystems.[7]
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.034 | 0.037 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".