Combinatory Chemical and Biological Approaches to Investigate Metal Elements in Agricultural Runoff Water
Bibliographic record
Abstract
As part of a project studying the interactions between farming practices, soil erosion processes, and fate of agricultural pollutants into runoff waters, we conducted a pilot study to investigate the relationship between metal contents and metallothionein-2A (MT-2A) as a bioindicator of metal exposure. Runoff water samples were collected between May and November 1999 at the point of outlet of an elementary watershed located in the Paris basin. Selected metals (Al, As, Cd, Cr, Cu, Pb, Hg, Ni, and Zn) were analyzed using conventional techniques. In parallel, human T cells were exposed to water samples for 6 and 18 h and then cell viability and MT-2A gene expression were measured. Results show that among the 10 water samples tested, Al and Zn predominate (highest values = 4.9 and 2.6 microM, respectively), while other metals were below the microM level. Five out of 10 samples induced MT-2A gene expression (30-80% increase at 18 h) as compared with the control. When comparing MT-2A induction profile with metals contents, no obvious correlation was found, suggesting that additional components or parameters are involved. Finally, there was an apparent inverse relationship between Ca concentration and MT-2A gene induction. Although still preliminary, in the absence of longer monitoring, this study shows that MT-2A gene expression is a useful tool to complement chemical analysis in assessing metal elements in water. These combinatory approaches will be pursued and integrated in an ongoing watershed field research project.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".