Haralick texture analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales
Bibliographic record
Abstract
This work demonstrates new avenues for their quantitative characterization of dosimetric distributions based on the underlying spatial distribution of data via texture analysis.Monte Carlo simulations are used to generate 3D (micro)dosimetric distributions on cellular and tumour length scales.Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are extracted from dosimetric distributions; sensitivity to Haralick analysis method and quantization Rowan, the years we worked together were filled with some exceptional circumstances and required numerous adaptations.Despite this, I can certainly say that you've made me feel supported and confident that we'd make it through over this entire journey.Thank you for being such a supportive, encouraging, and empathetic mentor in addition to being a truly spectacular supervisor.Dave, despite having the chance to meet and learn from you prior to joining the CLRP, I feel so lucky to have gotten the opportunity to get know you personally in the past years.Your knowledge and personality enrich the lab greatly.Thank you for the tremendous personal and professional support you've provided me over the years, in addition to being a great role model. I am very
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".