Improved scatter correction model for high attenuation gamma-ray tomography measurements
Bibliographic record
Abstract
Abstract In this study a state-of-the-art gamma-ray tomography (GRT) unit was used to measure the solids concentration distributions of high density, high attenuation clay/water/sand slurries in a 4 in. (100 mm) diameter recirculating pipe loop. The presence of neighbouring radiation sources on the GRT results in a scattered radiation contribution to the total intensity measured at each detector. The scattered radiation decreases the signal-to-noise ratio of the measured radiation intensity and introduces error in the measured tomography data and the reconstructed tomograms (Maad et al 2008 Meas. Sci. Technol. 19 6). Because of the high attenuation of the materials being measured, the scatter contribution was a significant fraction of the total radiation measured at each detector. To correct for the scattered radiation contribution in the measurements, a test campaign was undertaken to characterize and model the scattering behaviour of the GRT at the Saskatchewan Research Council. The scattered radiation was measured experimentally from empty pipe, water filled pipe and a number of flowing clay/water/sand slurries at densities ranging from 1206 kg m −3 to 1580 kg m −3 . A semi-empirical scatter correction model has been developed which allows the scattered contribution at each detector to be calculated iteratively based on the measured uncorrected attenuated radiation intensity. This article is an extension of the work presented at the 9th World Congress for Industrial Process Tomography (Spelay et al 2018 Proc. 9th World Congress Industrial Process Tomography p 14).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".