Error evaluation of partial scattering functions obtained from contrast-variation small-angle neutron scattering
Why this work is in the frame
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Bibliographic record
Abstract
Contrast-variation small-angle neutron scattering (CV-SANS) is a powerful tool to evaluate the structure of multi-component systems by decomposing the scattering intensities I measured with different scattering contrasts into partial scattering functions S of self- and cross-correlations between components. The measured I contains a measurement error Δ I , and Δ I results in an uncertainty in the partial scattering functions Δ S . However, the error propagation from Δ I to Δ S has not been quantitatively clarified. In this work, we have established deterministic and statistical approaches to determine Δ S from Δ I . We have applied the two methods to (i) computational data for a core–shell sphere, and experimental CV-SANS data of (ii) clay/polyethylene glycol aqueous solutions and (iii) polyrotaxane solutions, and have successfully estimated the errors in S . The quantitative error estimation in S offers a strategy to optimize the combination of scattering contrasts to minimize error propagation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it