Effect of detritus quality on growth and survival of gizzard shad (<i>Dorosoma cepedianum</i>): potential importance to benthic–pelagic coupling
Bibliographic record
Abstract
Gizzard shad (Dorosoma cepedianum) population characteristics vary with lake productivity, competing with and providing prey for sport fishes. Because age-0 gizzard shad (>30 mm total length) are facultative detritivores, they can link benthic energy, carbon, and nutrients to pelagic food webs. To determine how age-0 gizzard shad success varies along a detritus-quality gradient, we completed a 15-day laboratory experiment in which age-0 gizzard shad fed lake sediment and starved gizzard shad both suffered high mortality, whereas fish fed zooplankton grew and survived well. This suggested that detritus alone is insufficient to ensure gizzard shad growth and survival. When sediment quality was high in outdoor mesocosms, density-dependent factors led to rapid growth only at low fish density and high-quality sediments; however, survival generally increased with sediment quality, regardless of gizzard shad density. In four small reservoirs, annual growth of gizzard shad increased with sediment quality. Collectively, our findings suggest that detritus quality ultimately can contribute to regulation of community and ecosystem productivity, mediated by its influence on gizzard shad biomass available for trophic transfer to gape-limited predators (i.e., piscivorous fish). This role of gizzard shad can link higher trophic levels in aquatic food webs to allochthonous detritus subsidies from the surrounding watershed.
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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 | 0.000 |
| 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".