Manuscript for paper on the solution of one instance of the protein orientation problem
Bibliographic record
Abstract
This deliverable is meant to summarise our current conclusions about the cryo-microscopy + OAM sorter experiments. More specifically, we illustrate here how a two-stage OAM sorter allows one to recognise the rotational symmetry as well as the 2D chirality of proteins of interest. Getting information about a protein without actually fully imaging it is one of the main goals of Q-SORT. This deliverable is one of the partial endpoints of the activities developed so far and takes advantage of task T5.1 (D5.1 and D5.2) and of the sorter itself as developed in WP2. We also use virtual proteins, developed for deliverable D3.3. This deliverable has D5.5, devoted to protein recognition via three-stage or generalized sorter, as its natural continuation. The deliverable contains 2 parts. The first one is the main manuscript, ideally written for a journal of the Nature publishing Group. The second one is a provisional conclusion that explores the problem of the missing data, the hardware limitations, and improvements, as well as the feasibility of the overall experiments.
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.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.068 |
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".