The impact of dreissenid mussels on growth of the fragile papershell (<i>Leptodea</i> <i>fragilis</i>), the most abundant unionid species in Lake Erie
Bibliographic record
Abstract
The arrival of zebra mussels (Dreissena polymorpha (Pallas, 1771)) and subsequently quagga mussels (Dreissena bugensis Andrusov, 1897) (Dreissenidae) in the Great Lakes in the 1980s induced many changes, most notably the devastation of native freshwater mussel species. Recently, empty shells of the fragile papershell (Leptodea fragilis (Rafinesque, 1820)) have become common, particularly in the western basin of Lake Erie, suggesting that this fast-growing species may be increasing in numbers in the lake. To examine continued competition with dreissenids, shell age and length of L. fragilis were used to contrast lifespan and growth rate, estimated as the slope of age on shell length, for shells from two beach localities where byssal threads were present on most shells and two sites where dreissenids were rare or absent. Few recent shells from Lake Erie beaches exceeded 5 years of age, and byssal thread counts were more numerous on older shells. Growth and lifespan were estimated to be significantly lower where dreissenid mussels remained numerous than when measured either from historic collections along Lake Erie or from protected populations. Therefore, even for this early-reproducing species, competition from dreissenids may continue to interfere with growth and shorten lifespan, which are effects few other unionid species can likely tolerate sufficiently to sustain population growth.
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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 | 0.001 |
| 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".