Genetic evidence of widespread variation in ethanol metabolism among mammals: revisiting the ‘myth' of natural intoxication
Bibliographic record
Abstract
Humans have a long evolutionary relationship with ethanol, pre-dating anthropogenic sources, and possess unusually efficient ethanol metabolism, through a mutation that evolved in our last common ancestor with African great apes. Increased exposure to dietary ethanol through fermenting fruits and nectars is hypothesized to have selected for this in our lineage. Yet, other mammals have frugivorous and nectarivorous diets, raising the possibility of natural ethanol exposure and adaptation in other taxa. We conduct a comparative genetic analysis of alcohol dehydrogenase class IV (ADH IV) across mammals to provide insight into their evolutionary history with ethanol. We find genetic variation and multiple pseudogenization events in ADH IV, indicating the ability to metabolize ethanol is variable. We suggest that ADH enzymes are evolutionarily plastic and show promise for revealing dietary adaptation. We further highlight the derived condition of humans and draw attention to problems with modelling the physiological responses of other mammals on them, a practice that has led to potentially erroneous conclusions about the likelihood of natural intoxication in wild animals. It is a fallacy to assume that other animals share our metabolic adaptations, rather than taking into consideration each species' unique physiology.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".