Removing lead from the global economy
Bibliographic record
Abstract
Lead, an element toxic to countless biological processes, occurs naturally in the earth's lithosphere and is geologically sequestered from the biosphere at the earth's surface. When humans remove lead from the lithosphere and distribute it throughout the economy, its toxic effects impact throughout the web of life. Lead mining and manufacturing is a small industry that generates enormous harms. Lead impairs the growth, development, and reproduction of microbes, insects, plants, and animals. The annual human costs of lead exposure include 5·5 million premature adult deaths from cardiovascular disease and US$1·4 trillion in losses to the global economy from lead impairing children's cognitive development. Although the lead industry touts lead as the most recycled metal, most recycling occurs within countries that are incapable of enforcing environmental regulations. Millions of metric tonnes of lead are dispersed into the environment each year, disproportionately in low-income and middle-income countries. Substitutes for lead in the economy are available and we should act in the best interests of the planet and human health by eliminating lead from the global economy by 2035.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".