Comparative growth and feeding in zebra and quagga mussels (<i>Dreissena polymorpha</i>and<i>Dreissena bugensis</i>): implications for North American lakes
Bibliographic record
Abstract
In laboratory experiments, quagga mussels (Dreissena bugensis) survived as well as zebra mussels (Dreissena polymorpha) and equaled or exceeded their growth rate (3 to 242% change in wet mass) when reared at 6 or 23°C and fed natural seston or Chlamydomonas at food levels ranging from 0.057.4 µg·L 1 chlorophyll a (chl a). Superior growth of quagga mussels was most pronounced at low food levels. We found no significant differences in per capita clearance rates (CR), functional responses, or feeding behavior between zebra and quagga mussels fed Chlamydomonas, Nannochloris, or mixed suspensions of Nannochloris and clay. Per capita CR ranged from 0.018 to 0.402 L·mussel 1 ·h 1 for zebra mussels and from 0.010 to 0.407 L·mussel 1 ·h 1 for quagga mussels. Because quagga mussels had more biomass per unit shell length, we found lower biomass-specific CR for quagga mussels. When fed natural seston, zebra and quagga mussels could selectively reject inorganic material and at the lowest seston level the assimilation efficiency of quagga mussels (81%) was significantly higher than that of zebra mussels (63%). Our experiments suggest that quagga mussels can survive, grow, and feed as well or better than zebra mussels in epilimnetic waters with either low or high productivity.
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".