Aging clocks & mortality timers, methylation, glycomic, telomeric and more. A window to measuring biological age
Bibliographic record
Abstract
As humans age multiple forms of biological decay ensue, and many aspects of human biology can be measured to determine how far biological machinery has drifted from homeostasis. Research has led to aging clocks being developed that claim to predict biological age as opposed to chronological age. Aging could be regarded as a measured loss of homeostatic biological equilibrium that augments biological decay in fully developed tissues. Measuring aspects of how far various elements of biology have drifted from a youthful state may allow us to make determinations on a subject's health but also make informed predictions on their biological age. As we see across human physiology, many facets that maintain human health taper off such as nicotinamide adenine dinucleotide, glutathione, catalase, super oxide dismutase, and more. Extracellular vesicle density also tapers off during age combined with epigenetic drift, telomere attrition, and stem cell exhaustion, whilst genomic instability and biological insults from environment and lifestyle factors increase. Measuring these types of biomarkers with aging clocks may allow subjects to understand their own health more accurately and enable subjects to better focus on their efforts in the pursuit of longevity and, in addition, allow healthcare practitioners to deliver better health advice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".