Food web interactions between larval bluegill (<i>Lepomis macrochirus</i>) and exotic zebra mussels (<i>Dreissena polymorpha</i>)
Bibliographic record
Abstract
Food web interactions between native larval bluegill (Lepomis macrochirus), exotic invasive zebra mussels (Dreissena polymorpha), and zooplankton were examined with a mesocosm experiment. Hatchling larval bluegill collected from nests were reared in the presence of size-structured populations of zebra mussels in 1500-L limnocorrals suspended in an artificial pond for 2 weeks. Chlorophyll a, other limnological variables, and zooplankton abundance and biomass (including copepod nauplii and rotifers) were monitored over time. During their first 2 weeks of life, larval fish reared in the presence of mussels grew 24% more slowly than fish reared alone. Differential growth rates can be explained by competition between mussels and bluegill for food in the form of microzooplankton. Also likely was an indirect competition via starvation of the zooplankton community as zebra mussels consumed phytoplankton. Either direct or indirect trophic competition between zebra mussels and obligate planktivores may result in ecological harm as zebra mussels spread throughout inland lakes of North America.
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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 | 0.000 | 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) | 0.001 | 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".