Profiles of Biochemical Tracers in Unionid Mussels Across a Broad Geographical Range
Bibliographic record
Abstract
As large, long-lived filter feeders with high clearance rates, unionid mussels are capable of altering nutrient cycling in freshwater food webs. Because the effects of mussel communities on ecosystem processes result largely from their feeding behavior, we need to understand how they sample and process particulate matter. Our objective was to determine whether measuring a suite of biochemical markers in unionid mussels, from different populations of 2 species (Amblema plicata, Actinonaias ligamentina) across a broad geographical range (4 North American rivers), would help identify their primary dietary sources. Stable isotope data were able to differentiate mussels among rivers, and sometimes species. Data on δ15N revealed that mussels fed across trophic levels but that diet varied with habitat and perhaps species. Although mussels contained the same types of fatty acids regardless of river or species, we nonetheless observed considerable variation in essential fatty acid content across rivers. Essential fatty acid profiles were dominated consistently by arachidonic acid. Source-specific fatty acid biomarkers suggested that bacterial and detrital resources might be as important as algae in many habitats. In summary, biochemical metrics differed more among rivers than between species. This suggests that mussels are either considerably adaptable in their dietary requirements or that they feed nonselectively. Understanding nutritional profiles in mussels will help restore unionid communities, which may lead to the reestablishment of their critical role as nutrient recyclers in freshwater food webs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".