Evaluación temporal de la concentración de metales pesados (Pb y Cu) asociada con el sedimento vial: Fontibón-Barrios Unidos (Bogotá D. C., Colombia)
Bibliographic record
Abstract
Heavy metals associated with the road sediment can impair the quality of air, soil and vegetation of environment when they are suspended by the wind and turbulence induced by the traffic. Additionally, they can affect the water quality of the river systems when they are transported by the runoff. The objective of this paper is to present a temporary assessment (daily) of the heavy metals (Pb-Cu) concentration associated with the road sediment of the localities of Fontibon and Barrios Unidos (Bogota D.C., Colombia). The concentration was determined by flame atomic absorption spectrometry. Previously the samples were digested in a mixture of hydrochloric and nitric acid (3:1; aqua regia). The results show for the finest fraction of road sediment (< 63 µm), which is also the fraction with size closest to the potentially inhalable (< 10 µm), that the concentrations tend to increase in dry weather (Pb: 34%; Cu: 40%). The concentrations of Pb and Cu during this period are 1.59 and 5.30 times higher than the lowest limit value fixed by the administrations of Cataluna and Canada, respectively. The findings are a reference point for Colombia in order to publish legislation associated with this type of pollution (hazardous waste).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".