Comparative quantitation of DNA water tracers using OptiQ, Qubit, and Nanodrop
Bibliographic record
Abstract
Abstract We have recently developed new synthetic DNA tracers for tracking sources and pathways of contamination in surface water and groundwater. The use of DNA tracers in natural water systems results in substantial and rapid dilutions, thus accurate quantitation of initial DNA tracer concentrations applied is crucial to ensure their successful downstream detections. We compared the sensitivity and accuracy of three portable analytical techniques for quantitation of these DNA tracers: Nanodrop, Qubit, and OptiQ. All three methods were about equally effective when measuring high concentrations of DNA tracers (e.g., for c‐amine DNA tracer 1.54 × 105, 1.37 × 105, and 1.77 × 105 ng/mL for Nanodrop, Qubit, and OptiQ, respectively). However, the fluorescent methods of Qubit and OptiQ were significantly more sensitive at detecting lower concentrations of DNA tracers with limits of detection in the range 0.1–2 ng/mL, compared to 5 × 103 ng/mL for Nanodrop. The results of this work will facilitate the practical deployment of DNA tracers for tracking water contamination, and improving freshwater quality.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".