Effective atomic number of Mn–Co–Fe<sub>2</sub>O<sub>3</sub> ternary alloys using the Rayleigh to Compton scattering ratio
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
The objective of this work is to determine effective atomic number (Z eff ) of Fe 2 O 3(0.2) –Mn x –Co y (x + y = 0.8) ternary alloys using scattering of gamma photons and to compare available methods used to calculate Z eff . For this purpose, we have developed a fitting equation using the ratio of Rayleigh (R) to Compton (C) scattering intensity, R/C for the calculation of effective atomic number of ternary alloy (i.e., Mn–Co–Fe 2 O 3 ). R and C scattering intensities for the given materials have been measured using a mono-energetic beam of 59.54 keV gamma rays and a scattering angle of 130° (x = 4.36 Å −1 ). The R/C ratios of elements with 20 ≤ Z ≤ 30 were used to constitute the best fit equation. R/C scattering ratios, when plotted as a function of atomic number, results in a fitted equation, which is then used for derivation of Z eff of the alloys. Also, experimental R/C values were used to determine effective atomic number of the alloys by using interpolation procedure. For comparison, Z eff of alloys were also calculated using different methods. Maximum relative differences between Z eff for experimental and theoretical results were found to be ≤8.04% (exp. 1 (fitting) versus method 6) and were found to be ≤8.99% (exp. 2 (interpolation) versus method 7) indicating a good agreement for the chosen alloys.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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