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
In a previous work, we built a classifier that used a decision tree to predict fungal protein localization based on physiochemical properties of proteins. 178 features selected from proteins compositional properties, functional motifs and signal sequences were studied for their effect on subcellular localization. That work resulted in a localizer that would successfully predict some of the reported localizations in 64% of the cases and all the reported localizations in 49% of the cases. Here, we improve on the results of the mentioned work by streamlining the classes of protein features used. Considering various modes of intra-cellular protein movement and the requirements for such transport, we establish a list of features that would have direct impact on the recognition of the proteins by the transport machinery of the cell. We shall detect the occurrence of such features in fungal proteins and use them as potential determinants of subcellular localization. The system rebuilt based on 980 of such features is validated using a 5-fold cross validation and results in a success rate of 87% for predicting some and 77% for predicting all the reported localization sites of 3 fungal species for which annotations on subcellular localization were available.
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