Consumption of two exotic zooplankton by alewife (<i>Alosa pseudoharengus</i>) and rainbow smelt (<i>Osmerus mordax</i>) in three Laurentian Great Lakes
Bibliographic record
Abstract
Introductions of the zooplankton Bythotrephes longimanus and Cercopagis pengoi into the Great Lakes have drawn attention surrounding their suitability as prey for zooplanktivorous fishes. We used gut contents and stable carbon isotopes to quantify differential consumption and selection of the exotics by alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) in Lakes Erie, Michigan, and Ontario. The exotics were more often consumed by alewife (up to 70% of gut content biomass) than by smelt (up to 25% of gut content biomass). Measured stable carbon isotope ratios of fish and ratios predicted from mixing models confirmed that the snapshot descriptions of diet through gut contents were representative of longer-term diets. While B. longimanus generally was selected for (14 of 17 sampling dates), C. pengoi was not a preferred prey item. Cercopagis pengoi was sometimes a large component of alewife diet because of its high densities in the environment. The exotic zooplankton are more important for alewife than for smelt, and consumption of the cladocerans varies throughout the growing season and among lakes, generally related to patterns of exotic abundance. Effects associated with consumption of the exotics should be high in alewife-dominated systems invaded by B. longimanus or large numbers of C. pengoi.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".