Bibliographic record
Abstract
The ratio of macronutrients in an organism's diet can affect numerous phenotypes, including two of the most evolutionarily important traits: lifespan and reproductive output. Many organisms have been found to extend their lifespan through dietary restriction, specifically through caloric restriction. However, this increase in lifespan is accompanied by decreased reproductive output, because nutrient shortages cause a reallocation of resources from reproductive output to somatic maintenance (Shanley and Kirkwood 2000). However, research utilizing the geometric framework of nutrition has found that this effect is caused by a restriction of specific macronutrients rather than caloric restriction alone (Simpson and Raubenheimer 2012). Lifespan and reproductive effort require different macronutrient compositions in an organism's diet to be optimally expressed, so tradeoffs necessarily occur. Furthermore, males and females often require diets with very different macronutrient compositions to maximize reproductive effort. For example, male black field crickets (Teleogyllus commodus) maximize calling effort when fed diets high in carbohydrates, whereas females maximize egg production when fed diets high in protein. Despite these differences, the dietary preferences of male and female crickets are very similar (Maklakov et al. 2008).
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.014 |
| 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".