Method of Weighted Averages (Mosig–Michalski Extrapolation Algorithm)
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
This chapter introduces method of weighted averages (WA) which is currently known to be the most robust approach to evaluation of the Sommerfeld integrals (SIs). By mid-2010s WA approach reached maturity with extensive engineering literature available and practical implementations becoming easily attainable. It provides detailed derivations of identities foundational to the WA method and gives explanations for their use. The chapter describes the weighted average algorithm in its basic (single-level) form. Fast and accurate way to compute SIs is by using the partition-extrapolation method, which is an integration-then-summation method used in conjunction with an extrapolation method. Convergence of the sequence resulting from application of the partition and extrapolation method to evaluation of the Sommerfeld integral tails is classified for both the case of non-zero and zero radial distances.
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.
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) | 0.002 | 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