A Downhole Passive Sampling System To Avoid Bias and Error from Groundwater Sample Handling
Bibliographic record
Abstract
A new downhole groundwater sampler reduces bias and error due to sample handling and exposure while introducing minimal disturbance to natural flow conditions in the formation and well. This "In Situ Sealed", "ISS", or "Snap" sampling device includes removable/lab-ready sample bottles, a sampler device to hold double end-opening sample bottles in an open position, and a line for lowering the sampler system and triggering closure of the bottles downhole. Before deployment, each bottle is set open at both ends to allow flow-through during installation and equilibration downhole. Bottles are triggered to close downhole without well purging; the method is therefore "passive" or "nonpurge". The sample is retrieved in a sealed condition and remains unexposed until analysis. Data from six field studies comparing ISS sampling with traditional methods indicate ISS samples typically yield higher volatile organic compound (VOC) concentrations; in one case, significant chemical-specific differentials between sampling methods were discernible. For arsenic, filtered and unfiltered purge results were negatively and positively biased, respectively, compared to ISS results. Inorganic constituents showed parity with traditional methods. Overall, the ISS is versatile, avoids low VOC recovery bias, and enhances reproducibility while avoiding sampling complexity and purge water disposal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".