Bibliographic record
Abstract
The first publications describing gel dosimetry used magnetic resonance imaging to detect changes in the proton longitudinal relaxation rate of a gel infused with ferrous ions and irradiated with ionizing radiation. Later, different gel dosimeter systems were proposed that are based on the free-radical polymerization of monomers dispersed in a gel matrix. In these polymer gels, changes in transverse relaxation rates were shown to be dependent on the absorbed dose. More recently, contrast in MR images based on the exchange of magnetization between polymer and water protons, following saturation of the polymer protons, has been exploited in polymer gel dosimeters. In addition, variations in relaxation times in the rotating frame ( T 1ρ ) have been shown to produce contrast in MR images of irradiated polymer gel dosimeters. The signal and contrast in MR images may be manipulated to reflect a variety of these and other processes within an irradiated sample, An attempt is made here to provide an overview of the main different types of MRI contrast that may be used in gel dosimetry and, where possible, to relate this contrast to the nature of the chemical processes and structural changes that occur within the gels following the absorption of ionizing radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".