Why we should use Topological Data Analysis in Ageing: towards defining\n the "Topological shape of ageing"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Living systems are subject to the arrow of time; from birth, they undergo\ncomplex transformations (self-organization) in a constant battle for survival,\nbut inevitably ageing and disease trap them to death. Can ageing be understood\nand eventually reversed? What tools can be employed to further our\nunderstanding of ageing? The present article is an invitation for biologists\nand clinicians to consider key conceptual ideas and computational tools (known\nto mathematicians and physicists), which potentially may help dissect some of\nthe underlying processes of ageing and disease. Specifically, we first discuss\nhow to classify and analyze complex systems, as well as highlight critical\ntheoretical difficulties that make complex systems hard to study. Subsequently,\nwe introduce Topological Data Analysis - a novel Big Data tool - which may help\nin the study of complex systems since it extracts knowledge from data in a\nholistic approach via topological considerations. These conceptual ideas and\ntools are discussed in a rather informal way to pave future discussions and\ncollaborations between mathematicians and biologists studying ageing.\n
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it