Noise in the segmentation gene network of Drosophila with implications for mechanisms of body axis specification
Bibliographic record
Abstract
Specification of the anteroposterior (head-to-tail) axis in the fruit fly Drosophila melanogaster is one of the best understood examples of embryonic pattern formation, at the genetic level. A network of some 14 segmentation genes controls protein expression in narrow domains which are the first manifestation of the segments of the insect body. Work in the New York lab has led to a databank of more than 3300 confocal microscope images, quantifying protein expression for the segmentation genes, over a series of times during which protein pattern is developing (http://flyex.ams.sunysb.edu/FlyEx/). Quantification of the variability in expression evident in this data (both between embryos and within single embryos) allows us to determine error propagation in segmentation signalling. The maternal signal to the egg is highly variable, with noise levels more than several times those seen for expression of downstream genes. This implies that error suppression is active in the embryonic patterning mechanism. Error suppression is not possible with the favored mechanism of local concentration gradient reading for positional specification. We discuss possible patterning mechanisms which do reliably filter input noise.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".