Applying the Chelex-100 to Measure Trace Metals in High Salinity Samples in Southern Jhuoshuei River Alluvial Fan in Central Taiwan
Bibliographic record
Abstract
Due to the steep terrain and seasonal precipitation, retaining water on the surface in Taiwan is challenging. As a result, groundwater resources play a significant role in most types of water usage. However, historical data suggest that groundwater in the shallow layers near the coastal region of the South Jhuoshuei River Alluvial Fan has become widely salinized. This study collected multiple batches of water samples to analyze their characteristics and seasonal variations. The results indicate that the degree of salinization in the shallow layer is higher than in the deep layer. However, the data from the water samples only suggest that salinity is contributed to by saline water, without clarifying whether the source is lateral intrusion or surface contamination. To better understand salinity in this region, this study used Chelex-100 chromatography to reduce salinity and concentrate trace metals in the samples. The concentration of trace elements differs significantly between seawater, which has low levels, and fish farms, which exhibit higher levels. This distinction helps identify the source of the saline water. Initial tests showed that wells near Taixi had higher concentrations of trace elements, particularly Pb. In contrast, wells near Yiwu and Qiongpu displayed lower concentrations of trace elements. These findings suggest that salinization in the Taixi region is likely caused by anthropogenic sources, while salinization near Yiwu and Qiongpu results from lateral intrusion.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".