Purification of HTT N-HEAT_81-1643
Bibliographic record
Abstract
The purification of huntingtin (HTT) fragments is a useful approach to learn more about the function of HTT in the cell. By obtaining soluble and monomeric samples of HTT domains namely the C-HEAT, N-HEAT and bridge domains, specific protein-protein interactions can be studied. Furthermore, domains of HTT in soluble monomeric form could enable crystallization studies. The first expression and purification of these fragments can be found on these posts https://zenodo.org/record/2600051#.XKU89aeZPOQ and https://zenodo.org/record/2628060#.XULMtnspDb0 (performed by Dr. Rachel Harding). The latest post shows the purification of construct the HTT N-HEAT_81-1643 domain which elutes from Superdex 200 10/300 GL column in the void volume. The results here presented are a follow up of that purification.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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; both teacher heads agree on what is shown here.
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".