Sci‐YIS Fri ‐ 07: Fitting the linear‐quadratic model to detailed data set for different dose ranges
Bibliographic record
Abstract
Survival curve behaviour and degree of correspondence between Linear Quadratic model1 (LQ) and experimental data in an extensive dose range for high dose rate was analyzed. Detailed clonogenic assays with irradiation given in 0.5Gy increments and a total dose range varying from 10.5 and 16Gy were performed. The cell lines investigated were: CHOAA8, hamster fibroblast cells; U373MG, human glioblastoma cells; and human prostate carcinoma cell lines CP‐3, and DU‐145. The analyses were based on χ2 statistic and Monte Carlo simulation of the experiment. A decline of fit quality at very low doses (<2Gy) is observed. This result can be explained by the hypersensitive effect observed in CHOAA8 and U373MG data and an adaptive type response in CP3 cell line. A clear improvement is discerned at slightly higher doses. This could be a result of the linearity existent in the trend of survival curve at low doses, that will affect the total fit in a range from 0Gy to final dose in linear quadratic region. The impact of including low dose data is shown through α/β ratios showing relative differences of 42, 40 and 23% for CHOAA8, CP3 and U373MG. LQ model cannot explain survival at high doses. This is shown as a deterioration of goodness of fit for high doses. A comparison of Linear Quadratic Linear model2 (LQL) with the LQ in fitting the experimental data at high doses was also performed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".