Why aquatic scientists should use sulfur stable isotope ratios (ẟ34S) more often
Bibliographic record
Abstract
S) due to time, cost, or perceived lack of benefits and instrumentation capabilities. Using just carbon and nitrogen isotopic ratios can produce results that are inconclusive, uncertain, or in the worst cases, even misleading, especially for scientists that are new to the use and interpretation of stable isotope data. Using sulfur isotope values more regularly has the potential to mitigate these issues, especially given recent advancements that have lowered measurement barriers. Here we provide a review documenting case studies with real-world data, re-analysing different biological topics (i.e. niche, physiology, diet, movement and bioarchaeology) with and without sulfur isotopes to highlight the various strengths of this stable isotope for various applications. We also include a preliminary meta-analysis of the trophic discrimination factor (TDF) for sulfur isotopes, which suggest small (mean -0.4 ± 1.7 ‰ SD) but taxa-dependent mean trophic discrimination. Each case study demonstrates how the exclusion of sulfur comes at the detriment of the results, often leading to very different outputs, or missing valuable discoveries entirely. Given that studies relying on carbon and nitrogen stable isotopes currently underpin most of our understanding of various ecological processes, this has concerning implications. Collectively, these examples strongly suggest that researchers planning to use carbon and nitrogen stable isotopes for their research should incorporate sulfur where possible, and that the new 'default' isotope systems for aquatic science should now be carbon, nitrogen, and sulfur.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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; both teacher heads agree on what is shown here.
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".