Bibliographic record
Abstract
Childhood lead poisoning is a major public health concern in many countries. In 2015, the Hong Kong SAR Government and its citizens faced a major public health crisis due to the presence of lead in the drinking water of a number of public housing estates. Fortunately, no child was diagnosed with lead poisoning that required treatment with chelation. Lead is a ubiquitous, naturally occurring material that exists in air, dust, soil, and water. It is also widely present in industrial products including petrol, paints, ceramics, food cans, candies, cosmetics, traditional remedies, batteries, solder, stained glass, crystal vessels, ammunition, ceramic glazes, jewellry, and toys. It can also be found in human milk. There is no safe blood lead level and it may be impossible to completely eliminate lead from any city. Hence routine measurement of blood lead levels is not considered useful. Acute poisoning, especially with encephalopathy, deserves immediate medical treatment in hospital. Chelation therapy is recommended if blood lead level is 45 μg/dL or higher. For blood levels between 20 and 45 μg/dL, treatment is indicated if the child is symptomatic. For blood levels below 20 μg/dL in otherwise asymptomatic children, the principle of treatment is to provide long-term neurodevelopmental follow-up and counselling. In all cases, immediate removal of the source of lead exposure is vital. Even low levels of lead exposure can significantly impair learning, educational attainment, and neurodevelopment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".