Encephalization, expensive tissues, and energetics: An examination of the relative costs of brain size in strepsirrhines
Bibliographic record
Abstract
The evolution of encephalization requires that energetic challenges be met. Several hypotheses, such as the maternal energy and expensive tissue hypotheses, have been proposed to explain how some species are able to provide adequate energetic resources for large brains. The former incorporates maternal investment strategies, such as extended life history and elevated resting metabolic rate, which contribute to the growth of a large brain. The latter incorporates the reduction of gut size, which increases available energy for the maintenance of adult brain size. This study examines a sample of strepsirrhines, testing the hypothesis that encephalized species utilize some combination of the above-mentioned strategies. Infants and juveniles from three species at the Duke Lemur Center (DLC) were measured periodically to arrive at head and body growth trajectories. These data were used to determine the energetic tradeoff among the offspring. The examination of gestation length, weaning age, intestinal size and resting metabolic rate was used to assess adult brain maintenance and maternal energetic contribution. The results reveal that Daubentonia, the most encephalized and thus human-like of the lemurs, does not experience an energetic trade-off between brain and body during ontogeny, but does exhibit a trade-off between extensive brain growth and possibly reduced intestinal growth. Also, maternal energy is utilized. Encephalized lemurs, such as Daubentonia, have higher resting metabolic rate, while encephalized lorisiforms have a longer period of gestation. These results demonstrate that there are several strategies for meeting the energetic demands of encephalization, and they can be manifested differentially across taxa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".