Assessment of omega-3 PUFA reserves in the quagga mussel of Lake Ontario, with a special focus on its diet
Bibliographic record
Abstract
Long-chain polyunsaturated fatty acids of omega-3 family (n-3 LC-PUFAs), namely eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3), are physiologically essential compounds required for the normal growth and development of animals, including humans. These LC-PUFAs are mainly synthesized by some microalgae and transported through food webs. In contrast to many invertebrates, some bivalve molluscs have the ability to accumulate both EPA and DHA LC-PUFAs. Exotic bivalve quagga mussels invaded Lake Ontario in 1990 and reached lake-wide biomass of 910 ± 93 g × m−2 by 2023. We studied fatty acid (FA) level (% of total FAs) and content (mg × g−1 wet weight) in the quagga mussel across three depth zones of Lake Ontario: shallow (≤ 30 m), mid-depth (> 30−90 m), and deep (> 90 m). Fatty acid markers of diatoms, dinoflagellates, cryptophytes, green algae, cyanobacteria, and bacteria were found in the mussels in all depth zones but in different proportions, suggesting that mussel diets differed across depths. The level of EPA + DHA in quagga mussels was relatively high, comprising about 15% of total fatty acids. The EPA contents of quagga mussels varied across depth zones: shallow > mid-depth > deep while the DHA contents were invariant and comprised on average 0.6−0.7 mg × g−1 of wet weight. In total, 4500−5000 × 103 kg of n-3 PUFA are stored in quagga mussel biomass in Lake Ontario. However, this supply of n-3 PUFA is not fully utilized in the ecosystem because relatively small portion of quagga mussel biomass is consumed by predators in Lake Ontario.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".