Bias Assessment of Current Technologies Used for the Determination of Low Levels of Moisture in Mineral Oil Samples
Bibliographic record
Abstract
The problem in the current debate on the accuracy of Karl Fischer (KF) titrations lies in the fact that coulometry is being compared to volumetry on mineral oil samples for which the true moisture content is unknown. To clarify this point, dehydrated oil samples equilibrated under known temperature and relative humidity conditions and equilibrated oil samples containing known amounts of added moisture were used to assess the accuracy of the determinations. In addition, the measurements were extended to other techniques given that it is unlikely that they would be affected by the same phenomenon causing the KF systematic errors. The samples sent to different laboratories were analyzed by headspace/capillary gas chromatography, gas-phase H2 sensor, oil-phase or gas-phase RH sensors, KF coulometric titration with direct or indirect injection, and KF volumetric titration using a standard or NIST modified procedure. The laboratory comparison showed that with the exception of 4 techniques out of 10 that were tested, the measurements gave results in the expected concentration range. Considering the exceptions, two techniques based on volumetric titration yielded results tainted with an important positive bias for both sample types. This bias, tentatively associated with the high iodine end point concentration used by these systems, was estimated at approximately 22 ppm under the conditions applied by NIST. On the other hand, the two RH sensors showed a marked tendency to underestimate the value of the samples containing high moisture content. In this case, a loss of analyte through wall adsorption during the time required to achieve steady-state conditions in the measuring chamber seems to be at the origin of the negative biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".