Scenarios of metal concentrations in the Arcediano Dam (State of Jalisco, Mexico)
Bibliographic record
Abstract
The city of Guadalajara, Jalisco, Mexico has 4.1 million inhabitants and a serious deficit in water supply. Once constructed, the Arcediano Dam will catch waters from the Verde and Santiago rivers, and after treatment will provide water to the city. The present study was undertaken to formulate scenarios and estimate risks of polluting the water that will be collected in the dam from the release of contaminants accumulated in sediments. Desorption of metals from sediments was estimated through sampling of water and sediments, chemical analyses of the environmental samples, and numerical modeling of the water-sediment interactions. Water quality generally increased as the river flowed downstream from the El Salto sampling station to the site where the Arcediano Dam will be constructed. Aluminum exceeded the Mexican Criterion for Drinking Water Supply (MCDWS), at all sampling stations, whereas iron and manganese surpassed the criteria at some stations. Trace metals were below their respective criteria. For sediment samples in the river, chrome, copper and zinc exceeded the Canadian Interim Sediment Quality Guidelines (ISQG), whereas manganese and nickel exceeded the probable effect level (PEL). Other metals were below these limits. With exception of the El Salto sampling station, metals were mostly enriched in iron-containing sediments, followed by aluminum-containing clays and, to a lesser extent, by manganese oxides. Therefore, the interaction of metals with iron oxides was considered as the controlling adsorption mechanism. Simulations to estimate risks of water contamination by desorption of metals from sediments indicate that manganese and nickel may reach concentrations higher than the CDWS. In some cases, dissolved concentrations of lead and chrome may be higher than the respective CDWS, especially under conditions of high accumulation of sediments in the dam. Arsenic, copper and mercury concentrations did not exceed the CDWS under the simulated conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".