Bibliographic record
Abstract
A major focus of this release is performance. Not so much the speed of running a simulation, but everything else surrounding it. Creating a System from a ForceField is much faster than before. Building water boxes and membranes are both much faster. Loading PDB files is a bit faster. In certain situations, creating a new Context can be a lot faster. And so on. This release adds classes for simulated tempering and metadynamics. These are the same classes that were previously available as separate downloads. They are now bundled as standard features. This release adds support for PPC processors. This is mainly to support Summit, since a lot of people want to run on there. We've also added a major feature that involves a huge amount of new code, but won't yet actually be useful for most people: we've implemented all the functional forms used in the HIPPO force field. This is a new force field under development by Jay Ponder's group, which is designed to be the successor to AMOEBA. It should be a lot more accurate and more transferable, while also being faster. They're still working on parametrizing the force field, so you can't yet run simulations with it, but we have all the code ready to support it once the force field is finished.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.279 | 0.402 |
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".