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
PA provides 2 main services: •Analysis ( ) •Upload sequences in fastA format •Process the sequences with tools (runs BLAST, Prosite) •Parse tokens from the tool’s output •Use tokens to predict the class of the protein (Ex. Hydrolase Activity, Cytoplasm) using Machine Learning. •Provide an Explanation ( ) for the prediction •Custom Classifer Creation ( ) •Upload Labeled sequences in fastA format •Process the sequences with tools (runs BLAST, Prosite) •Parse tokens from the tool’s output and use them to detect similarities within classes using Machine Learning. •Use detected similarities to classify new proteins with unknown properties. What does PA do? PA recently finished training a new Gene Ontology (GO) Function classifier. •12 Classes •102,225 sequence training set •Built using EBI’s GO mapping & the SwissProt database •Precision: 93% •Recall: 97% Also see Proteome Analyst’s Gene Ontology Poster Gene Ontology Function
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