Data from: The effects of river algae and porewater flow on the feeding of juvenile mussels
Bibliographic record
Abstract
Juvenile mussels enter the benthos after excysting from a fish host and settling to the bottom where they inhabit the interstitial zone in rivers. We examined the algal composition in the surface water and pore waters in different locations in a temperate river (Thames River) in Southern Ontario. Surprisingly, algal concentration (C) was ~9× higher in pore water versus surface water, varied spatially in the riverbed (downstream of boulders > upstream of boulders and non-bedform regions), and pennate diatoms were the most abundant taxon in the pore waters. We examined the clearance rate (CR; mass of suspended material removed from the water per unit time and mussel) of recently metamorphosed juvenile unionid mussels (3 – 4 week old Lampsilis siliquoidea, Fatmuckets) exposed to pore water and surface water in a paddle-wheel flow chamber at different water velocities (U). Juvenile CR based on chlorophyll a fluorescence was ~2× higher on pore water versus surface water and CR based on a specific algal taxon, identified via flow cytometry, varied with its initial concentration. Chesson’s feeding electivity index revealed that mussels removed 5 chlorophyte taxa in proportion to their concentration in the water (i.e., removed at random) but they removed 5 diatom taxa in greater proportion (i.e., selected for by juvenile mussels) across the range of algal flux (J = UC) examined. This study provides evidence of the importance of diatoms in pore waters to juvenile mussels. It also reveals elements of the physico-chemical environment used by juvenile mussels, which should be considered in their conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.020 | 0.004 |
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".