Natural variation in food acquisition mediated<i>via</i>a<i>Drosophila</i>cGMP-dependent protein kinase
Bibliographic record
Abstract
In natural environments where food abundance and quality can change drastically over time, animals must continuously alter their food acquisition strategies. Although genetic variation contributes to this plasticity, the specific genes involved and their interactions with the environment are poorly understood. Here we report that natural variation in the Drosophila gene, foraging (for), which encodes a cGMP-dependent protein kinase (PKG), affects larval food acquisition in an environmentally dependent fashion. When food is plentiful, the wild-type rover (for(R)) allele confers lower food intake and higher glucose absorption than both the wild-type sitter (for(s)) allele and the mutant for(s2) allele. When food is scarce, for(R), for(s) and for(s2) larvae increase food intake to a common maximal level, but for(R) larvae retain their increased absorption efficiency. Changes in for expression can induce corrective behavioral modifications in response to food deprivation. When reared in environments with low food levels, for(R) larvae have higher survivorship and faster development than for(s) and for(s2) larvae. Together, these results show that natural variation in for has far reaching implications affecting a suite of phenotypes involved in the regulation of food acquisition.
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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 | 0.000 |
| 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".