Healthy Aging – Insights from Drosophila
Bibliographic record
Abstract
Human life expectancy has nearly doubled in the past century due, in part, to social and economic development, and a wide range of new medical technologies and treatments. As the number of elderly increase it becomes of vital importance to understand what factors contribute to healthy aging. Human longevity is a complex process that is affected by both environmental and genetic factors and interactions between them. Unfortunately, it is currently difficult to identify the role of genetic components in human longevity. In contrast, model organisms such as C. elegans, Drosophila, and rodents have facilitated the search for specific genes that affect lifespan. Experimental evidence obtained from studies in model organisms suggests that mutations in a single gene may increase longevity and delay the onset of age-related symptoms including motor impairments, sexual and reproductive and immune dysfunction, cardiovascular disease, and cognitive decline. Furthermore, the high degree of conservation between diverse species in the genes and pathways that regulate longevity suggests that work in model organisms can both expand our theoretical knowledge of aging and perhaps provide new therapeutic targets for the treatment of age-related disorders.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".