HISTOGRAM-BASED SCORING SCHEMES FOR PROTEIN NMR RESONANCE ASSIGNMENT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In NMR protein structure determination, after the resonance peaks have been identified and chemical shifts from peaks across multiple spectra have been grouped into spin systems, associating these spin systems to their host residues is the key toward the success of structural information extraction and thus the key to the success of the structure calculation. To achieve accurate enough structure calculation, a near complete and accurate assignment is a prerequisite. There are two pieces of information that can be used into the assignment, one of which is the adjacency information among the spin systems and the other is the signature information of the spin systems. The signature information reflects the fact that, generally speaking, for one type of amino acid residing in a specific local structural environment, the chemical shifts for the atoms inside the amino acid fall into some very narrow distinct ranges. In most of the existing work, normal distributions are assumed with means and standard deviations statistically collected from the available data. In this paper, we followed a simple yet effective histogram-based way to estimate for every spin system the probability that its host is a certain type of amino acid residing in a certain type of secondary structure. We used two combinations of chemical shifts to demonstrate the effectiveness of this type of histogram-based scoring schemes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it