Application of Multi-stable Isotope (13C, 15N, 34S, 37Cl) Assays to Assess Spatial Separation of Fish (Longnose Sucker Catostomus catostomus) in an Area Receiving Complex Effluents
Bibliographic record
Abstract
Abstract Incorporation of stable isotope analysis (SIA) into routine environmental effects monitoring programs of receiving waters may enable determination of the spatial extent of biotic exposure and discrimination among sources of complex effluents. To evaluate this hypothesis, longnose sucker (Catostomus catostomus) were collected from four sites along the Athabasca River, Alberta (upstream reference site, two sites downstream of effluents from two pulp and paper mills, and a site downstream of effluent from a municipal sewage treatment plant). Stable isotopes of carbon, nitrogen, sulfur and chlorine were analyzed in bone, gonad, liver and white muscle tissues of the fish. In general, most sites and tissues differed according to δ13C, δ15N and δ34S values. Also, an interaction between site and tissue was observed for δ15N values. A better insight into the usefulness of stable isotopes was obtained through the use of multivariate discriminant function analysis. δ15N and δ34S signatures of gonad and liver tissues of males were most effective at classifying fish according to site (~70% for both tissues). For all tissues except bone, fish from the upstream reference site were most separable from all others, especially females. δ37Cl values for female gonads and male livers were related to sites downstream of the pulp and paper mills. Future research should routinely include SIA of fish tissues, but also of effluents, receiving waters and food web components to better resolve links between specific effluents and fish exposure.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".