An alternative to the “Star Path” enhancement of the ADMA linear scaling method for protein modeling
Bibliographic record
Abstract
With the aim of improving the performance of macromolecular quantum chemistry conformation analysis and reaction path following methods, the Adjustable Density Matrix Assembler (ADMA) method has already been combined with some faster although less accurate density matrix extrapolation methods, such as the Löwdin-Inverse-Löwdin (LIL) extrapolation along a potential energy surface, and a strategically arranged back-and-forth switching between these methods has been proven to be advantageous. Here, an alternative approach is proposed and investigated, based on several actual test calculations, where the "inexpensive" LIL density matrix extrapolation steps are replaced by only somewhat more expensive, but still ADMA-based calculations, where in the "rough-search stage," only interactions of shorter distances within the macromolecule are considered. It is shown that this approach is viable, as an alternative to the "Star Path" method including both ADMA and LIL steps. © 2017 Wiley Periodicals, Inc.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".