MétaCan
Menu
Back to cohort
Record W2418860793 · doi:10.1038/srep19441

The nutritional and hedonic value of food modulate sexual receptivity in Drosophila melanogaster females

2016· article· en· W2418860793 on OpenAlexafffund
Jenke A. Gorter, Samyukta Jagadeesh, Christoph L. Gahr, Jelle J. Boonekamp, Joel D. Levine, Jean‐Christophe Billeter

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsBiologyDrosophila melanogasterOffspringReceptivityDrosophila (subgenus)Reproductive successReproductionNutrientFood scienceEcologyGeneticsEndocrinologyMedicinePregnancyEnvironmental health

Abstract

fetched live from OpenAlex

Food and sex often go hand in hand because of the nutritional cost of reproduction. For Drosophila melanogaster females, this relationship is especially intimate because their offspring develop on food. Since yeast and sugars are important nutritional pillars for Drosophila, availability of these foods should inform female reproductive behaviours. Yet mechanisms coupling food and sex are poorly understood. Here we show that yeast increases female sexual receptivity through interaction between its protein content and its odorous fermentation product acetic acid, sensed by the Ionotropic odorant receptor neuron Ir75a. A similar interaction between nutritional and hedonic value applies to sugars where taste and caloric value only increase sexual receptivity when combined. Integration of nutritional and sensory values would ensure that there are sufficient internal nutrients for egg production as well as sufficient environmental nutrients for offspring survival. These findings provide mechanisms through which females may maximize reproductive output in changing environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.290
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations98
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicNeurobiology and Insect Physiology ResearchFrench-language works237,207