Interlaboratory Comparison of Branched GDGT Temperature and pH Proxies Using Soils and Lipid Extracts
Bibliographic record
Abstract
Abstract Ratios of glycerol dialkyl glycerol tetraethers (GDGT), which are membrane lipids of bacteria and archaea, are at the base of several paleoenvironmental proxies. They are frequently applied to soils as well as lake‐ and marine sediments to generate records of past temperature and soil pH. To derive meaningful environmental information from these reconstructions, high analytical reproducibility is required. Based on submitted results by 39 laboratories from across the world, which employ a diverse range of analytical and quantification methods, we explored the reproducibility of brGDGT‐based proxies (MBT′5ME, IR, and #ringstetra) measured on four soil samples and four soil lipid extracts. Correct identification and integration of 5‐ and 6‐methyl brGDGTs is a prerequisite for the robust calculation of proxy values, but this can be challenging as indicated by the large inter‐interlaboratory variation. The exclusion of statistical outliers improves the reproducibility, where the remaining uncertainty translates into a temperature offset from median proxy values of 0.3–0.9°C and a pH offset of 0.05–0.3. There is no apparent systematic impact of the extraction method and sample preparation steps on the brGDGT ratios. Although reported GDGT concentrations are generally consistent within laboratories, they vary greatly between laboratories. This large variability in brGDGT quantification may relate to variations in ionization efficiency or specific mass spectrometer settings possibly impacting the response of brGDGTs masses relative to that of the internal standard used. While ratio values of GDGT are generally comparable, quantities can currently not be compared between laboratories.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".