Bibliographic record
Abstract
A letter dated July 2006 from Dr. Fernando Díaz-Barriga Martínez of the Department of Environmental Toxicology in the Department of Medicine at the University of San Luis Potosí in Mexico. He writes to Herman Zerpa, mayor (or perhaps manager) of the town of Abra Pampa, Argentina. The letter describes the preliminary results of the soil sample testing. The letter states that the levels of lead in the soil is above the acceptable level and recommends an environmental clean-up. The letter also recommends an immediate public health program including monitoring the levels of lead in children's blood. It states that the soil samples were also sent to a Canadian institution for testing for 50 elements. The letter emphasizes the urgency of the situation and states that no one should wait for the results from Canada to act. The writer reminds the mayor that children are most susceptible to the effects of lead and that its main effect is on neural and cognitive functioning. The letter suggests contacting Dr. Graciela Bovi Mitre of the University of Jujuy and Dr. Susana García at the Ministry of Health. The letter appears to have been sent to these two people as well. Within the letter and also attached is a list of the results of 10 soil samples with their concentration of lead and cadmium.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".