Contingency table techniques for three dimensional atom probe tomography
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
A contingency table analysis procedure is developed and applied to three dimensional atom probe data sets for the investigation of fine-scale solute co-/anti-segregation effects in multicomponent alloys. Potential sources of error and inaccuracy are identified and eliminated from the technique. The conventional P value testing techniques associated with chi(2) are shown to be unsatisfactory and can become ambiguous in cases of large block numbers or high solute concentrations. The coefficient of contingency is demonstrated to be an acceptable and useful basis of comparison for contingency table analyses of differently-conditioned materials. However, care must be taken in choice of block size and to maintain a consistent overall composition between experiments. The coefficient is dependent upon block size and solute composition, and cannot be used to compare analyses with significantly different solute compositions or to assess the extent of clustering without reference to that of the randomly ordered case. It is shown that as clustering evolves into larger precipitates and phases, contingency table analysis becomes inappropriate. Random labeling techniques are introduced to infer further meaning from the coefficient of contingency. We propose the comparison of experimental result, mu(exp), to the randomized value, micro(rand), as a new method by which to interpret the quantity of solute clustering present in a material. It is demonstrated that how this method may be utilized to identify an appropriate size of contingency table analysis blocks into which the data set is partitioned to optimize the significance of the results.
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 | 0.002 | 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