Impact of zebra mussels (<i>Dreissena polymorpha</i>) on the pelagic lower trophic levels of Oneida Lake, New York
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We analyzed a data series on nutrients, phytoplankton, zooplankton, and young-of-the-year fish from Oneida Lake, New York, to test several hypotheses relating the response of the pelagic food web to grazing by zebra mussels (Dreissena polymorpha). System-wide grazing rates increased by one to two orders of magnitude after zebra mussel introduction. The most dramatic change associated with dreissenid grazing was increased water clarity and overall decrease in algal biovolume and Chl a. Contrary to predictions, primary production did not decline. We attribute the lack of whole water column decline in primary productivity to the compensating effect of increased water clarity resulting in deeper penetration of photosynthetically active radiation. We observed no change in total or dissolved phosphorus concentrations. Although algal standing crop declined, Daphnia spp. biomass and production did not, but dominance shifted from Daphnia galeata mendotae to Daphnia pulicaria. Consistent with our findings in the lower food web, we found no evidence that zebra mussels had a negative impact on young yellow perch (Perca flavescens) growth, biomass, or production. Thus, despite the order of magnitude increase in grazing rates and associated decrease in algal biomass, pelagic production at primary, secondary, and tertiary levels did not decline in association with 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it