Mercury and selenium in three fish species from a dam 20 months after a mine-tailing spill in the SE Gulf of California ecoregion, Mexico
Bibliographic record
Abstract
Abstract During January 2013, a mining spill occurred in the Santa Maria mining region, releasing around 300,000 m3 of tailings on Los Remedios river, which was transported through the San Lorenzo river and finally to El Comedero (EC) dam. Twenty months later, we examined the concentrations of Hg and Se in the muscle, liver, gills, and guts of three fish species (Cyprinus carpio, Oreochromis aureus, Micropterus salmoides) captured in the EC dam to assess the performance of the cleaning operations. A high Se concentration in the liver of all species (carp, 1.2 ± 0.4; tilapia, 3.9 ± 2.1; bass, 3.5 ± 1.1 µg g−1 ww) was consistently observed, while this behavior was only found in the blue tilapia for Hg (0.15 ± 0.11 µg g−1 ww). Tilapia (benthic-detritivorous) exhibited the highest Se concentrations compared to the carp (omnivore) and the largemouth bass (piscivore). In contrast, the largemouth bass had the highest Hg levels in the muscle compared with the other fishes. Such differences could be related to the different metabolism and feeding habits among species. Compared to a tilapia study carried out three months after the mine spill during a mortality event, a decrease was evident in the liver for Se and Hg by 7.2 and 4.7 times, respectively. This reveals that cleaning operations were more efficient for Se and less for Hg, and that a prolonged period was required for the partial recovery of the element levels in fish from sites impacted by mining. Considering the Mexican consumption scenarios for each fish species, it could be concluded that there will be no non-cancer risk by exposure to Hg or Se.
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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 | 0.000 |
| 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) | 0.001 | 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".