A global compilation of known-origin keratin hydrogen and oxygen isotope data for wildlife and forensic research
Bibliographic record
Abstract
<p>Variations in stable hydrogen (δ<sup>2</sup>H) and oxygen (δ<sup>18</sup>O) isotope ratios have been used in wildlife and forensic applications to infer the provenance of biological tissues by comparing isotopic measurements for unknown samples to geographically indexed measurements or predictions. Tissues composed of the structural protein keratin have been targeted in many systems, leading to a legacy of published data for known-origin samples. An open synthesis of these data would be useful to support broader analysis of keratin isotope patterns across biological systems and as a reference data collection for future studies.</p><p>Significant differences in sample preparation and analysis protocols and calibration and normalization approaches among laboratories have created substantial challenges in the integration of these data, however. Here we identify and assess factors that might be limiting comparability of δ<sup>2</sup>H and δ<sup>18</sup>O data among laboratories. These include sample type and sampling method, procedure for lipid extraction, whether and how partial exchange of keratin H with atmospheric moisture has been addressed, which laboratory reference materials have been used, drying and handling protocols, analysis method, and quality of chromatography for O isotopic analyses. We compile a list of reference materials (including Utah, USGS, and Saskatoon standards) and their established values, and develop a set of ‘rules’ and corrections to account for differences in processing methods and standards as well as the associated uncertainty. We apply these corrections to more than 2500 known-origin data from the literature and demonstrate that the comparability of isotopic data among laboratories is greatly improved by linking all measurements to the same scales. We highlight both the potential of the harmonized dataset for use in wildlife and forensic research as well as substantial challenges and limitations that remain.</p>
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".