A Method to Distinguish Chromium‐Tanned Leathers With Low and High Risks of Surface Hexavalent Chromium
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
INTRODUCTION: Traces of hexavalent chromium, Cr(VI), are a major concern for skin contact with Cr-tanned leather. Current extraction methods (ISO 17075-1:2017) for Cr(VI) testing do not necessarily reflect the true potential of surface-formed Cr(VI), as extracted concentrations are dependent on previous storage and atmospheric conditions. OBJECTIVES: To test whether a spiking method protocol can distinguish leathers with high and low risks of releasing Cr(VI). METHODS: Two groups of leather types were selected based on previously detected Cr(VI) (group A) and optimal tanning practices with high antioxidants (group B), corresponding to a high and low risk of forming and keeping Cr(VI). Leathers were spiked with different concentrations up to 10 mg/kg of Cr(VI) and incubated at 80°C for 24 h prior to the ISO 17075-1:2017 extraction protocol. RESULTS: All Cr(VI) was reduced by group B leathers, whereas all group A leather extracts contained detectable Cr(VI) that was dependent on the exact leather type and the amount initially spiked. CONCLUSION: Pre-treatment of samples with supplemental Cr(VI) is a potential method for determining the reduction capabilities of leather, which are closely related to the risk of Cr(VI) formation. 10 mg/kg spiking unambiguously distinguished leathers with high and low risks of forming Cr(VI).
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) | 0.000 | 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