Sulphur stable isotopes can distinguish trophic dependence on sediments and plankton in boreal lakes
Bibliographic record
Abstract
Summary 1. Stable isotopes of carbon are useful for differentiating between freshwater food chains based on planktonic algae or benthic algae, but are reported to be of limited use for identifying food chains based on sedimentary detritus. Because data from marine systems suggest that stable isotopes of sulphur (δ34S values) have potential in this regard, we tested their utility in freshwater lakes. 2. We found that sulphate in the water column of four boreal lakes was enriched in 34S compared to the sulphur in bulk sediments from these lakes. Furthermore, within a given lake, insects known to feed on sediment (directly or via predation) had δ34S values similar to those of sediment, whereas planktonic and benthic invertebrates known to feed on suspended particles had δ34S values similar to those of sulphate in the water column. 3. Using the stable S isotope values of invertebrates that obtain their S from either the sediment or the water column as end members in a two‐source mixing model, we show that two fish species obtain their food from both planktonic and sedimentary sources. Furthermore, model results suggest that, as expected, the more benthic‐feeding fish species obtains more of its S from the sediment compartment than does the species that feeds in the water‐column. 4. Our results suggest that measurements of stable sulphur isotopes provide a means of distinguishing between members of food chains that are based in the water column from those based on sedimentary detritus. As such, they would be a useful complement to stable C isotopes that are used to distinguish between food chains based on planktonic or benthic algae.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".