Variation in growth rates of the zebra mussel, <i>Dreissena polymorpha</i>, within Lake Wawasee
Bibliographic record
Abstract
1. Field experiments conducted in Lake Wawasee in 1995 and 1996 measured the response of shell growth of Dreissena polymorpha to environmental gradients. 2. Shell growth decreased with initial shell length in four mussel size classes ranging between 8 and 22 mm, and decreased with depth, with mussels in shallow water (<4 m) having growth rates nearly twice those of mussels in deeper water (4–7 m). 3. Growth occurred early in the spring–summer period (May–June) with relatively little shell added later in the summer (July–September), and varied significantly among sites within Lake Wawasee, but not between the 2 years of this study. 4. Rank order of sites was consistent for both years implying that environmental conditions responsible for variation in shell growth were stable within Lake Wawasee. 5. Cage design did not have a significant effect on mussel shell growth nor did the distance of growth cages above the bottom (0.5–0.75 m above the bottom versus directly on the bottom). 6. This study demonstrates the sensitivity of adult mussel growth to subtle variation in environmental conditions occurring within and among lakes, with potential consequences for mussel population dynamics and community structure and function.
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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".