Does size matter? Particle size vs. quality in bivalve suspension feeding
Bibliographic record
Abstract
Abstract Increases in total suspended solids (TSS) in rivers have likely contributed to the decline in unionid mussel population sizes as feeding and reproduction are reduced at high TSS concentrations. Surprisingly, however, unionids are often found in turbid rivers. We predicted that clay‐sized particles, which comprise > 80% of river seston (particles in suspension), were too small to affect unionid clearance rate (CR) and thus explain this conundrum. We examined this hypothesis in laboratory experiments involving four unionid species exposed to four particle‐size based TSS treatments (mixed sediment: 0–63 μm; clay: 0–5 μm; fine silt: 5–38 μm; coarse silt: 38–63 μm; sourced from mussel sites) at 20 mg/L, which is a TSS concentration sufficient to reduce CR. Whereas the CR for all species was lower for mixed sediment, coarse silt and clay treatments—the latter was opposite to our prediction—the CR of mussels given the fine silt treatment was similar to the no‐TSS control for two species and higher than the other TSS treatments in the other two species. Fine silt contained the most fluorescent (i.e. algal) particles and the highest protein and lipid content, which suggests that CR were higher on the more nutritious diet. Particle quality, rather than size, is what modulates suspension feeding in turbid rivers. Our current understanding of the ecological effects of bivalve suspension feeding will need to be revised to incorporate field‐based measurements.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".