Efficiency of sympagic-benthic coupling revealed by analyses of n-3 fatty acids, IP25 and other highly branched isoprenoids in two filter-feeding Arctic benthic molluscs: Mya truncata and Serripes groenlandicus
Bibliographic record
Abstract
The aim of this work was to determine the impact of sympagic (ice-associated) algal primary production on the quality of Arctic filter-feeding bivalves. For this purpose, we investigated the sea ice production of lipids (including omega-3 polyunsaturated fatty acids (n-3 PUFA) and highly branched isoprenoids (HBI)), as well as their subsequent incorporation into the truncate softshell clam ( Mya truncata ) and the Greenland cockle ( Serripes groenlandicus ), during the melting periods of two consecutive years in Baffin Bay. Lipid and primary production exhibited seasonal variability and overall contrasts between the two years, as a result of distinct physical forcings and the ensuing biological responses. Whilst less productive in terms of total lipids or chlorophyll a , spring 2016 was more productive than spring 2015 for n-3 PUFA , which are essential for benthic fauna. The sea ice diatom HBI biomarker IP 25 was quantified in sea ice from both years. Interestingly, such production was preceded by a production of the hitherto ‘pelagic’ biomarker, HBI III, in sea ice. In bivalves, HBI contents and correlations confirmed the tightness of the Arctic sympagic-benthic coupling and highlighted that S. groenlandicus can be used as a sentinel species for assessing the degree of this coupling. The confirmation that bivalves incorporate sea-ice derived HBI III and not only IP 25 , may introduce uncertainties into the use of some HBI-based indices. Monitoring of the fatty acid contents of bivalves allowed identification of their spawning periods and suggests that M. truncata did not store enough n-3 PUFA to sustain its reproductive effort.
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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 | 0.001 |
| 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".