Proteomic and Transcriptomic Techniques to Decipher the Molecular Evolution of Venoms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nature's library of venoms is a vast and untapped resource that has the potential of becoming the source of a wide variety of new drugs and therapeutics. The discovery of these valuable molecules, hidden in diverse collections of different venoms, requires highly specific genetic and proteomic sequencing techniques. These have been used to sequence a variety of venom glands from species ranging from snakes to scorpions, and some marine species. In addition to identifying toxin sequences, these techniques have paved the way for identifying various novel evolutionary links between species that were previously thought to be unrelated. Furthermore, proteomics-based techniques have allowed researchers to discover how specific toxins have evolved within related species, and in the context of environmental pressures. These techniques allow groups to discover novel proteins, identify mutations of interest, and discover new ways to modify toxins for biomimetic purposes and for the development of new therapeutics.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it