Peer Review #2 of "fLPS 2.0: rapid annotation of compositionally-biased regions in biological sequences (v0.2)"
Bibliographic record
Abstract
Compositionally-biased (CB) regions in biological sequences are enriched for a subset of sequence residue types.These can be shorter regions with a concentrated bias (i.e., those termed 'low-complexity'), or longer regions that have a compositional skew.These regions comprise a prominent class of the uncharacterized 'dark matter' of the protein universe.Here, I report the latest version of the fLPS package for the annotation of CB regions, which includes added consideration of DNA sequences, to label the eight possible biased regions of DNA.In this version, the user is now able to restrict analysis to a specified subset of residue types, and also to filter for previously annotated domains to enable detection of discontinuous CB regions.A 'thorough' option has been added which enables the labelling of subtler biases, typically made from a skew for several residue types.In the output, protein CB regions are now labelled with bias classes reflecting the physicochemical character of the biasing residues.The fLPS 2.0 package is available from: https://github.com/pmharrison/flps2or in a supplementary file of this paper.
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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 | 0.012 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.409 | 0.315 |
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".