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 Gd 3+ ion with spin S=7/2 is exploited here to calculate the kernel signals for the various distances, r, between two coupled Gd 3+ radicals, distributed randomly in a biological system. These are then used to estimate the probabilities of the distance distribution, P ( r ), between the various Gd 3+ 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 D 2 O/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 Gd 3+ 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.
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 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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".