Bibliographic record
Abstract
Mathematical modelling of tumour growth has been studied by many scientists using a wide variety of techniques and approaches. In this thesis, I examine the role of “chaotic attractors,” a term more commonly seen in the context of physics, using a model of three cell types: host, immune, and tumour. The relationships between these cell populations are derived from the law of mass action, often found in chemistry, assuming that a conjugate is formed in the interaction between immune and tumour cells. Just based on this quick holistic overview, the interdisciplinary nature of modern cancer research is displayed. My research, structured as an undergraduate thesis in Physics, seeks to combine multiple disciplines to develop a model and explain its underlying significance in the important real-world problem of cancer. This analysis is carried out computationally using mathematics and computer programming, but its significance is in the clinical setting, where similar models have been used by previous groups to analyse individual case studies. My work, still very much in progress, has as a goal to investigate the full set of possible parameters, so as to develop a deeper understanding of the underlying mechanisms inherent to this model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".