An Assessment of Stream Water Quality of the Rio San Juan, Nuevo Leon, Mexico, 1995–1996
Bibliographic record
Abstract
Good water quality of the Rio San Juan is critical for economic development of northeastern Mexico. However, water quality of the river has rapidly degraded during the last few decades. Societal concerns include indications of contamination problems and increased water diversions for agriculture, residential, and industrial water supplies. Eight sampling sites were selected along the river where water samples were collected monthly for 10 mo (October 1995-July 1996). The concentration of heavy metals and chemical constituents and measurements of bacteriological and physical parameters were determined on water samples. In addition, river discharge was recorded. Constituent concentrations in 18.7% of all samples exceeded at least one water quality standard. In particular, concentrations of fecal and total coliform bacteria, sulfate, detergent, dissolved solids, Al, Ba, Cr, Fe, and Cd, exceeded several water quality standards. Pollution showed spatial and temporal variations and trends. These variations were statistically explained by spatial and temporal changes of constituent inputs and discharge. Samples collected from the site upstream of El Cuchillo reservoir had large constituent concentrations when discharge was small; this reservoir supplies domestic and industrial water to the city of Monterrey.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".