Physiological energetics of Lake Erie dreissenid mussels: a basis for the displacement of <i>Dreissena</i> <i>polymorpha</i> by <i>Dreissena bugensis</i>
Bibliographic record
Abstract
I measured respiration, shell growth, body mass, and reproduction in sympatric populations of zebra (Dreissena polymorpha) and quagga (Dreissena bugensis) mussels in western Lake Erie to determine if the species differ in physiological parameters and if any differences provide an explanation for the recent displacement of zebra mussels by quagga mussels. Between May and August, I measured respiration (1998), shell growth of marked mussels suspended in cages in the lake (19992000), soft body mass and mass of gametes released (19982001), and the number of gametes released by individual mussels (1999). Quagga mussels had lower respiration rates and greater shell growth and body mass. There was no difference in the percentage of spawning mussels or the number of sperm released by individuals, but zebra mussels generally released more eggs and a greater mass of gametes than did quagga mussels. Similar reproduction at a lower body mass indicates that zebra mussels devote a greater proportion of body tissue to reproduction. Lower respiration rates and larger size give quagga mussels a competitive advantage and may explain their displacement of zebra mussels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".