What it means to be marine: Sulfur isotope variability in the historical Chesapeake Bay ecosystem
Bibliographic record
Abstract
Stable sulfur isotope ( δ 34 S) analysis is an important tool for addressing archaeological and ecological questions about diet and mobility. A growing body of work has underscored the value of δ 34 S for tracing food sources linked to specific kinds of aquatic primary production, including saltmarshes, freshwater wetlands, seagrass beds, and benthic microalgal communities. Comparatively little work has investigated δ 34 S variation in other marine vertebrate food webs. This may reflect prevailing assumptions that isotopically homogenous marine sulfates will be the main contributor to consumer δ 34 S in all marine ecosystems. We explore this assumption through δ 34 S, δ 13 C, and δ 15 N analyses of bone collagen from a taxonomically and ecologically broad cross-section of marine fauna from preindustrial contexts at the archaeological site of Jamestown, near the Chesapeake Bay in present-day Virginia. Results for most taxa show δ 34 S values that diverge from those expected based on species’ marine ecologies. Benthic primary production, serving as a vector for sulfide-influenced, low- δ 34 S sulfur entering aquatic food webs, offers the most parsimonious explanation. The ecological diversity represented in these findings, covering a wide range of marine and estuarine habitats, suggests that this phenomenon could be common where benthic algae form an important part of primary production in estuarine and other coastal habitats across the globe. Implications for archaeological and ecological δ 34 S interpretive frameworks are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".