A Method to Distinguish Chromium‐Tanned Leathers With Low and High Risks of Surface Hexavalent Chromium
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).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".