Modern plants and sulfur isoscapes — A review, discussion, and construction of a pilot δ <sup>34</sup> S isoscape for mobility and provenance studies
Bibliographic record
Abstract
RATIONALE Sulfur isotopes are increasingly used as mobility indicators in humans and animals in biology, archaeology, and forensics. However, there has been a lack of modern sulfur isotope baseline “isoscape” studies using modern plants and animals, largely due to the possibility of contamination of the S isotope values by modern pollution. METHODS We collected plants from across a 900‐km east–west transect of British Columbia Canada and measured their sulfur isotope values. We then used a random forest model to determine which variables best explained the isotope data patterning and produced a sulfur isoscape for the southern region of British Columbia. RESULTS We see clear patterning in the plant sulfur isotope values that relate to geographical location and rainfall. Our model also shows that for this study area, it is unlikely that there is a significant influence of anthropogenic pollution on plant δ 34 S values. We also discuss the use of plants as a substrate for sulfur isoscapes and possible explanations for the often‐observed difference between plant and animal δ 34 S values from the same region, related to differing sources of sulfur in plants compared to amino acids in human and animal tissues. CONCLUSIONS We found that for areas of the world where sulfur pollution is likely less widespread, it is possible to produce a modern plant S isoscape that should be an accurate baseline for mobility studies. Using random forest modelling, we have produced a baseline sulfur isoscape map of southern British Columbia that can be used for ecology, forensic and archaeological studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".