SU‐E‐T‐98: Towards Cell Nucleus Microdosimetry: Construction of a Confocal Laser‐Scanning Fluorescence Microscope to Readout Fluorescence Nuclear Track Detectors (FNTDs)
Bibliographic record
Abstract
PURPOSE: :C,Mg fluorescent nuclear track detector (FNTD). The spatial resolution of the FNTD technique is at the sub-micrometer scale. Therefore the FNTD technique has the potential to perform radiation measurements at the cell nucleus scale. METHODS: centers after trapping delta electrons from ionizing radiation. These centers have an absorption band centered at 620 nm and an emission band in the near infrared. Events of energy deposition in the crystal are read-out using a CLSM with sub-micrometer spatial resolution. Excitation light from a 635 nm laser is focused in the crystal volume by an objective lens. Fluorescence is collected back through the same path, filtered through a dichroic mirror, and focused through a small pinhole onto an avalanche photodiode. Lateral scanning of the focal point is performed with a scanning mirror galvanometer, and axial scanning is performed using a stepper-motor stage. Control of electronics and image acquisition was performed using a custom built LabVIEW VI and further image processing was done using Java. The system was used to scan FNTDs exposed to a 6 MV x-ray beam and an unexposed FNTD. RESULTS: Fluorescence images above the unexposed background were obtained at scan depths ranging from 5 - 10 micrometer below the crystal surface using a 100 micrometer pinhole size. CONCLUSIONS: Further work needs to be done to increase the resolution and the signal to noise ratio of the images so that energy deposition events may be identified more easily. Natural Sciences and Engineering Research Council of Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".