1Characterizing DNAPL in Ground Water Using Partitioning Fluorescent
Bibliographic record
Abstract
Locating and characterizing dense nonaqueous phase liquids (DNAPLs) is one of the most important challenges in groundwater remediation. Partitioning tracers is one way to meet this task. In this study, five fluorescent dyes were evaluated as partitioning tracers for detection and quantification of tetrachloroethylene (PCE): fluorescein, rhodamine WT (RWT), sulforhodamine B (SRB), eosine, and pyranine. Fluorescein was used as a conservative tracer. Batch tests were performed to obtain sorption partitioning coefficients (KP) and NAPL/water partitioning coefficients (KNW) for each tracer. Retardation factors (Rf) were predicted using column tests with Ottawa sand as a porous medium. KP values were 0.088 + 0.025, and 0.091 cm3/g for RWT and SRB respectively. Eosine and pyranine increased fluorescence after soil/dye batch tests; thus, KP could not be obtained for these tracers. Background fluorescence and pH apparently did not have influence in this behavior. KNW values for RWT, SRB, and eosine were 0.1703, 0.0603, and 0.049 respectively. Pyranine also increased fluorescence in PCE/dye tests. Retardation factors measured in column tests for fluorescein, RWT, SRB, eosine and pyranine were a) 1.08 + 0.07, 1.64 + 0.38, 1.23, 1.45, and 2.97, in absence of PCE; and b) 1.31, 2.3, 1.33, 1.79, and 3.51, in presence of PCE, respectively. The results of this study suggest that the partitioning behavior of the tracers tested is different from simple hydrophobic partitioning. Column tests indicated that RWT, SRB, and eosine are possibly suitable as partitioning tracers.
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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 | 0.001 |
| 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 | 0.000 |
| 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".