Comparison of Results for Quarter 1 Surface Water Split Samples Collected at the Nuclear Fuel Services Site, Erwin, Tennessee
Bibliographic record
Abstract
Oak Ridge Associated Universities (ORAU), under the Oak Ridge Institute for Science and Education (ORISE) contract, collected split surface water samples with Nuclear Fuel Services (NFS) representatives on August 22, 2012. Representatives from the U.S. Nuclear Regulatory Commission and Tennessee Department of Environment and Conservation were also in attendance. Samples were collected at four surface water stations, as required in the approved Request for Technical Assistance number 11-018. These stations included Nolichucky River upstream (NRU), Nolichucky River downstream (NRD), Martin Creek upstream (MCU), and Martin Creek downstream (MCD). Both ORAU and NFS performed gross alpha and gross beta analyses. The comparison of results using the duplicate error ratio (DER), also known as the normalized absolute difference. A DER ≤ 3 indicates that, at a 99% confidence interval, split sample results do not differ significantly when compared to their respective one standard deviation (sigma) uncertainty. The NFS split sample report does not specify the confidence level of reported uncertainties. Therefore, standard two sigma reporting is assumed and uncertainty values were divided by 1.96. A comparison of split sample results, using the DER equation, indicates one set with a DER greater than 3. A DER of 3.1 is calculated for gross alpha results from ORAU sample 5198W0003 and NFS sample MCU-310212003. The ORAU result is 0.98 ± 0.30 pCi/L (value ± 2 sigma) compared to the NFS result of -0.08 ± 0.60 pCi/L. Relatively high DER values are not unexpected for low (e.g., background) analyte concentrations analyzed by separate laboratories, as is the case here. It is noted, however, NFS uncertainties are at least twice the ORAU uncertainties, which contributes to the elevated DER value. Differences in ORAU and NFS minimum detectable activities are even more pronounced. comparison of ORAU and NFS split samples produces reasonably consistent results for low (e.g., background) concentrations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".