Benthic and pelagic contributions to <i>Mysis</i> nutrition across Lake Superior
Bibliographic record
Abstract
Quantifying nutritional sources for Mysis diluviana will help to clarify the basis for production in lakes with Mysis and improve models of migration-driven nutrient and contaminant transport. We sampled Mysis, plankton, and benthos across Lake Superior using a stratified-random design that provided a statistically valid representation of the lake across depths. We then estimated nutritional contributions to Mysis using stable isotope ratios of Mysis, zooplankton, Bythotrephes , Diporeia , oligochaetes, and detritus in a multiple-source, dual-isotope mixing model. Lake-wide, small (<1.0 cm) mysids relied almost exclusively upon plankton, whereas large mysids occupied a higher trophic position and obtained nutrition among sources. Model estimates of mean benthic contributions to large Mysis ranged from 27% to 58%. We predicted the importance of benthos to Mysis to track declining benthic biomass with depth. Model results indicated that if Diporeia were the only benthic food eaten, benthic contributions would decline to 40% with depth, but inclusion of detritus in the model resulted in consistent importance of benthic food across depths. The importance of benthos to mysid nutrition suggests strong benthic–pelagic coupling at all lake depths and might limit the ability of Mysis to support fisheries in systems that have lost Diporeia.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".