Bibliographic record
Abstract
The intuitive attraction of biological age may be its potential to serve as a unifying factor guiding our understanding of biological aging. A while ago, the problem of biological age determination attracted some attention before returning to obscurity for more than two decades. New ‘omics’ based technologies make a large number of biological traits available, and the recent attempts to use such traits in assessing biological age in individuals (e.g., DNA methylation patterns, protein profining) are promising. In this symposium, we assembles a team of experts in various aspects of aging, from animal models (SEH), epidemiology and epigenetic (MEL), system biology of enery metabolism (SMJ), and mathematical modeling of aging (AM). In addition to presenting examples of biological age determinationin different systems, the participants will discuss the problem of data integration from multiple sources taking into account the computational algorithms used for determination of biological age. The following questions will be addressed. How informative are the different traits that are used to address biological age? How can different algorithms of biological age be compared? Could different organ-based measures of biological age be unified? What are the prospects of using biological age measures in interventions to control (e.g. slow down, postpone) the aging process? Finally. does biological age ever exists as an objective biological measure or is it just a metaphore for hetergeneity of health status in individuals? The presentations cover a broad range of topics and discussion will contribute to an understanding of biology of aging.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".