Following the propagation of erroneous x-ray photoelectron spectroscopy peak fitting through the literature. A genealogical approach
Bibliographic record
Abstract
This study considers how poor x-ray photoelectron spectroscopy (XPS) peak fitting in the scientific literature is both affected by previous precedent and affects future published work. It focuses on a highly cited paper (the “Subject” paper) from a respected journal that contains incorrect S 2p peak fits. This paper was studied in a genealogical fashion vis-à-vis the XPS peak fitting in its “child,” “parent,” “grandparent,” and “great-grandparent” papers. Interestingly, precedents were not followed to a high degree between parent and child papers. However, in many cases, even when the authors of a study did not follow the incorrect precedent that they cited, they still incorrectly fit their data. Thus, not necessarily for good reasons, the effects of poor XPS peak fits on future generations of papers may be less than some experts had expected or feared. In many cases, older papers appear to contain better XPS peak fitting than newer ones.
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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 | 0.083 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".