Interspecies Chemical Signals Released into the Environment may Create Xenohormetic, Hormetic and Cytostatic Selective Forces that Drive the Ecosystemic Evolution of Longevity Regulation Mechanisms
Bibliographic record
Abstract
Various organisms (i.e., bacteria, fungi, plants and animals) within an ecosystem can synthesize and release into the environment certain longevity-extending small molecules. Here we hypothesize that these interspecies chemical signals can create xenohormetic, hormetic and cytostatic selective forces driving the ecosystemic evolution of longevity regulation mechanisms. In our hypothesis, following their release into the environment by one species of the organisms composing an ecosystem, such small molecules can activate anti-aging processes and/or inhibit pro-aging processes in other species within the ecosystem. The organisms that possess the most effective (as compared to their counterparts of the same species) mechanisms for sensing the chemical signals produced and released by other species and for responding to such signals by undergoing certain hormetic and/or cytostatic life-extending changes to their metabolism and physiology are expected to live longer then their counterparts within the ecosystem. Thus, the ability of a species of the organisms composing an ecosystem to undergo life-extending metabolic or physiological changes in response to hormetic or cytostatic chemical compounds released to the ecosystem by other species: 1) increases its chances of survival; 2) creates selective forces aimed at maintaining such ability; and 3) enables the evolution of longevity regulation mechanisms.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".