Effective atomic number of Mn–Co–Fe<sub>2</sub>O<sub>3</sub> ternary alloys using the Rayleigh to Compton scattering ratio
Bibliographic record
Abstract
The objective of this work is to determine effective atomic number (Zeff) of Fe2O3(0.2)–Mnx–Coy (x + y = 0.8) ternary alloys using scattering of gamma photons and to compare available methods used to calculate Zeff. 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–Fe2O3). 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 Zeff of the alloys. Also, experimental R/C values were used to determine effective atomic number of the alloys by using interpolation procedure. For comparison, Zeff of alloys were also calculated using different methods. Maximum relative differences between Zeff 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".