A simple and inexpensive technique for assessing contamination during drilling operations
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 Subsurface exploration relies on drilling. Normally drilling requires a drilling fluid that will infiltrate into the drill core. Drilling fluid contains non‐indigenous materials and microbes from the surface, so its presence renders a sample unsuitable for microbiological and many other analyses. Because infiltration cannot be avoided, it is of paramount importance to assess the degree of contamination to identify uncontaminated samples for geomicrobiological investigations. To do this, usually a tracer is mixed into the drilling fluid. In past drilling operations a variety of tracers have been used, each has specific strengths and weaknesses. For microspheres the main problem was the high price, which limited their use to spot checks or drilling operations that require only small amounts of drilling fluid. Here, we present a modified microsphere tracer approach that uses an aqueous fluorescent pigment dispersion with a similar concentration of fluorescent particles as previously used microsphere tracers. However, it costs four orders of magnitude less, allowing for a more liberal use even in large operations. Its applicability for deep drilling campaigns was successfully tested during two drilling campaigns of the International Continental Drilling Program (ICDP) at Lake Towuti, Sulawesi, Indonesia, and Lake Chalco, Mexico. Quantification of the tracer requires only a fluorescence microscope or a flow cytometer. The latter allowing for high‐resolution data to be obtained directly on‐site within minutes and with minimal effort, decreasing sample processing times substantially relative to traditional tracer methods. This approach offers an inexpensive, rapid, but powerful alternative technique for contamination assessment during drilling campaigns.
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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 | 0.001 | 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 | 0.001 | 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