Logistic regression for the modeling of low-dose radiation effects.
Bibliographic record
Abstract
The primary goal of this thesis is to examine statistical procedures that determine if a nonlinear threshold dose effect occurs in cancer incidence in animal experiments. This is to say, does there exist a level of radiation which does not increase the risk of cancer in animals? This question will be explored on data over several species of animals (beagles, rats, and mice) and several different sources of radiation (e.g. radium, or plutonium). Here we consider experiments with several groups of animals at several dose levels In mathematical terms, this thesis will determine if a "knee" fit to a logistic regression models the data better than a simple logistic regression curve. The knee is a non-linear kink in the dose response relationship at a specific dose. If there is evidence to support the knee model hypothesis, it can be interpreted to mean that there is evidence of a threshold effect in the dose response. A secondary goal is to determine if two knees will suffice for data sets of this nature. The motivation behind this goal, is that a two knee model should be able to model both a low dose threshold and a high dose drop-off (due to over exposure to radiation, past the Maximally Tolerated Dose, MTD). Any knees above two will generally only contribute to overfitting of the model. Another goal is to give guidelines on the design of experiments, that will assist researchers in determining if a threshold does in fact exist.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".