Natural chemical markers identify source and date of introduction of an exotic species: lake trout (<i>Salvelinus namaycush</i>) in Yellowstone Lake
Bibliographic record
Abstract
Exotic species invasions pose a pervasive threat to aquatic ecosystems worldwide, yet fundamental questions about the geographic origin and timing of invasions or introductions are frequently difficult to answer. We used natural chemical markers (Sr:Ca ratios) in otoliths to identify probable source and date of introduction of exotic lake trout (Salvelinus namaycush) into Yellowstone Lake, Wyoming, USA. Otolith Sr:Ca ratios were significantly different among lake trout known to have reared in three different Yellowstone National Park lakes (Yellowstone, Heart, and Lewis lakes) and showed little variation along the otolith axis, indicating that lake trout within each lake experienced a similar, and consistent, environmental history. In contrast, suspected transplants showed a large and rapid increase in otolith Sr:Ca ratios indicative of a marked shift to water of differing chemistry. Timing of the abrupt change in Sr:Ca ratios indicated that some lake trout were introduced into Yellowstone Lake during the late 1980s, but more recent transplants also occurred. A discriminant model identified Lewis Lake as the likely source lake for lake trout transplanted into Yellowstone Lake. Our results demonstrate that chemical signatures within otoliths can serve as an important forensic tool for identifying the probable source and date of exotic fish introductions.
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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 | 0.000 |
| 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".