Freshwater wetland–driven variation in sulfur isotope compositions: Implications for human paleodiet and ecological research
Bibliographic record
Abstract
Sulfur isotope (δ34S) analyses are an important archaeological and ecological tool for understanding human and animal migration and diet, but δ34S can be difficult to interpret, particularly in archaeological human-mobility studies, when measured isotope compositions are strongly 34S-depleted relative to regional baselines. Sulfides, which accumulate under anoxic conditions and have distinctively low δ34S, are potentially key for understanding this but are often overlooked in studies of vertebrate δ34S. We analyze an ecologically wide range of archaeological taxa to build an interpretive framework for understanding the impact of sulfide-influenced δ34S on vertebrate consumers. Results provide the first demonstration that δ34S of higher-level consumers can be heavily impacted by freshwater wetland resource use. This source of δ34S variation is significant because it is linked to a globally distributed habitat and occurs at the bottom of the δ34S spectrum, which, for archaeologists, is primarily used for assessing human mobility. Our findings have significant implications for rethinking traditional interpretive frameworks of human mobility and diet, and for exploring the historical ecology of past freshwater wetland ecosystems. Given the tremendous importance of wetlands’ ecosystem services today, such insights on the structure and human dynamics of past wetlands could be valuable for guiding restoration work.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".