Comparative quantitation of DNA water tracers using OptiQ, Qubit, and Nanodrop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 × 10 5 , 1.37 × 10 5 , and 1.77 × 10 5 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 × 10 3 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it