Estimation of distance distributions between Gd3+ radical pairs with a significant zero- field splitting from pulsed EPR DEER data
Bibliographic record
Abstract
Abstract The numerical method, using full diagonalization of the spin-Hamiltonian matrix, to calculate DEER (Double Electron-Electron Resonance) signal, based on the double rotating-frames (DRF) technique, taking into account the zero-field splitting of the Gd3+ ion with spin S=7/2 is exploited here to calculate the kernel signals for the various distances, r, between two coupled Gd3+ radicals, distributed randomly in a biological system. These are then used to estimate the probabilities of the distance distribution, P(r), between the various Gd3+ radical pairs, separated by the distances, r. This is accomplished by using Tikhonov regularization, as implemented in the software DeerAnalysis [Jeschke et al. Appl. Magn. Reson., 30(3), pp.473-498 (2006)], but using the kernel signals calculated here by the DRF technique for different r values. This procedure is successfully illustrated by applying it to calculate the distance distribution probabilities, P(r) from the reported experimental four-pulse DEER data for a sample of Gd ruler 1_5 in D2O/glycerol-d_8 (i) at Q-band [Doll et al., J. Magn. Reson. 259, pp.153-162] and (ii) at W-band [Dalaloyan et al., Phys. Chem. Chem. Phys., 17(28), pp.18464-18476 (2015)]. Significant differences in the distance-distribution probabilities are found between those calculated here using the DRF-calculated kernel signals for Gd3+ with spin 7/2 with significant ZFS from those calculated by the use of analytical kernel signals for spin-½ system without ZFS, using hard pulses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".