Bibliographic record
Abstract
Seminal fluid proteins (Sfp) are central in animal reproduction, from insects to mammalian species. In thecontext of the analysis of the functional properties of the Sfps from an evolutionary perspective, weinvestigated how they interact, paying attention to the architecture of the resulting network. The topologyof the Sfp proteome consists in several subnetworks, which were examined based on the evolutionaryage composition of their constituent genes, i.e. when these genes appeared during the fly phylogeny. Sfp-encoding genes were categorized based on their evolutionary age within five age classes: class A, genespresent before the Drosophila radiation; class B, genes originated before the split between the Drosophilaand Sophophora subgenera; class C, genes formed in early divergent lineages leading to D. willistoni andD. pseudoobscura species groups; class D, genes originated in the melanogaster species group; andclass E, genes present only in the D. melanogaster species subgroup, including the simulans speciescomplex and D. melanogaster. Through Monte Carlo simulations, we interrogated whether the observedevolutionary age composition of different subnetworks was expected by chance alone or there was asignificant enrichment for particular age classes in different subnetworks of the Sfp proteome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.026 |
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".