Bibliographic record
Abstract
Evolution represents a natural experimental process for testing animal design features. Driven by environmental pressures, animals have evolved adaptations which can give valuable insights into human biomedical conditions. The giraffe by virtue of its extremely long neck has a mean arterial pressure much higher than other mammals. However, the giraffe does not develop vascular damage or heart failure despite its high mean arterial pressure. The giraffe's cardiovascular physiology challenges a number of current concepts concerning the genesis of hypertensive vascular damage in the human. All animals senesce, and, in general, the manifestations of this senescence are similar to the aging features observed in humans. The characteristics of aging in natural animals strongly suggest that the so-called chronic degenerative diseases of humans are not really diseases but actually manifestations of the aging phenotype. Glucose regulation in birds and the naked mole rat has features which mimic the characteristics of the diabetic state, yet these animals do not develop the complications occurring in humans with diabetes. Disruptions in the functioning of the circadian molecular clock are thought to underlie certain neuropsychiatric disorders. The honeybee and the zebrafish have emerged as natural animal models for studying the regulation of molecular clocks and the mechanisms underlying plasticity of circadian rhythms. These examples underscore the valuable insights that natural animals can furnish with respect to biomedical disorders. Yet, this information data base remains a largely untapped resource.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".