Terrestrial support of zebra mussels and the Hudson River food web: A multi‐isotope, Bayesian analysis
Bibliographic record
Abstract
The Hudson River is a strongly heterotrophic system in which the invasive zebra mussel (Dreissena polymorpha) comprises >90% of total metazoan biomass. Using a Bayesian mixing model, with isotope ratios of C, N, and H, and four basal resources (phytoplankton, benthic algae, submersed aquatic vegetation [SAV], and terrestrial inputs), we estimated the reliance of 10 consumers on each resource. Copepods, Bosmina, and herring (Alosa aestivalis) relied 40–60% on phytoplankton primary production; amphipods and young‐of‐year white perch (Morone spp.) relied heavily on benthic algae (50–60%). Terrestrial detritus was an important resource for oligochaetes, zebra mussels, chironomids, and Bosmina sp., for which median estimates of reliance were between 40% and 60%. The dual reliance of zebra mussels on terrestrial detritus and phytoplankton production, combined with their high biomass, along with the significant terrestrial support of several other consumers, indicates that terrestrial detritus supports a significant portion of the Hudson River food web. Nonetheless, given that particulate and dissolved organic matter pools are heavily dominated (60–80%) by terrestrial detritus, it is clear that selectivity by consumers for autochthonous organic matter is generally high. Despite its large biomass and productivity, we did not find strong evidence for support of the food web by SAV.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".