Investigating Improvement in Gadolinium Detection for a XRF Bone Measurement System by Averaging Spectrum
Bibliographic record
Abstract
An X-ray fluorescence (XRF) measurement system involving a high purity germanium detector (HPGe) was used to quantify gadolinium and lanthanum in bone. The 24-hour ex vivo minimum detection limit (MDL) was estimated to be 1.0 µg Gd g−1 bone mineral determined from single measurements of low concentration gadolinium hydroxyapatite (HAp) calibration standards. It was thought that the average of 60 measurements at 24-minutes (equivalent to 24-hours) may improve the detection limit and signal to noise ratio (SNR). Curve fitting procedures applied to the 24-minute spectrum reduced the uncertainty in measurement. Detection limits from a simple averaging method were compared to the application of the inverse variance weighted mean (IVWM). The IVWM is an aggregating method that could be considered a system optimization with 60 replicates. In either case, with the assumption that gadolinium was uniformly distributed in the phantom material, the MDL was estimated to be 0.8 µg Gd g−1 bone mineral from the aggregate methods.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".