Normalization of Energy-Dependent Gamma Survey Data
Bibliographic record
Abstract
Instruments and methods for normalization of energy-dependent gamma radiation survey data to a less energy-dependent basis of measurement are evaluated based on relevant field data collected at 15 different sites across the western United States along with a site in Mongolia. Normalization performance is assessed relative to measurements with a high-pressure ionization chamber (HPIC) due to its "flat" energy response and accurate measurement of the true exposure rate from both cosmic and terrestrial radiation. While analytically ideal for normalization applications, cost and practicality disadvantages have increased demand for alternatives to the HPIC. Regression analysis on paired measurements between energy-dependent sodium iodide (NaI) scintillation detectors (5-cm by 5-cm crystal dimensions) and the HPIC revealed highly consistent relationships among sites not previously impacted by radiological contamination (natural sites). A resulting generalized data normalization factor based on the average sensitivity of NaI detectors to naturally occurring terrestrial radiation (0.56 nGy hHPIC per nGy hNaI), combined with the calculated site-specific estimate of cosmic radiation, produced reasonably accurate predictions of HPIC readings at natural sites. Normalization against two to potential alternative instruments (a tissue-equivalent plastic scintillator and energy-compensated NaI detector) did not perform better than the sensitivity adjustment approach at natural sites. Each approach produced unreliable estimates of HPIC readings at radiologically impacted sites, though normalization against the plastic scintillator or energy-compensated NaI detector can address incompatibilities between different energy-dependent instruments with respect to estimation of soil radionuclide levels. The appropriate data normalization method depends on the nature of the site, expected duration of the project, survey objectives, and considerations of cost and practicality.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".