Bibliographic record
Abstract
Researchers are counting on comparative biologists to find alternative animal models of human aging that will foster experimental approaches to study disability-free longevity, not just the addition of years. This article presents one such alternative: the use of pet dogs living in the same environment as people to study the determinants of healthy longevity. There are both theoretical and practical reasons for this research model beyond the well-documented physiologic similarities between dogs and humans. First, a wealth of medical data--based on clinical and biochemical evaluation, medical imaging, and pathology--is available for pet dogs. Second, a vast array of phenotypic domains can be accurately assessed in dogs, ranging from cardiac contractility and glomerular integrity to the ability to climb stairs and interact with people. Moreover, studying pet dogs obviates the purchase and per diem costs typically associated with large animal research. Pet dogs may be particularly well suited for exploring (1) mechanisms of sex differences in longevity; (2) interventions to compress morbidity and enhance healthspan; (3) genomic correlates of successful aging phenotypes and endophenotypes; (4) heterogeneity in resistance to aging-related diseases, such as cancer; and (5) noninvasive biomarkers of particular target organs. Finally, between-breed differences in senescence trajectories and longevity may expand hypotheses of key genetic factors that contribute to sustained organ function and the postponement of disease. Yet the pet dog paradigm in aging research is nascent; tapping into the potential of this model will add to the existing strengths of conventional model systems.
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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".