Biomarkers reveal the effects of hydrography on the sources and fate of marine and terrestrial organic matter in the western Irish Sea
Bibliographic record
Abstract
A suite of lipid biomarkers were investigated from surface sediments and particulatematter across hydrographically distinct zones associated with the western Irish Sea gyre and the seasonal bloom. The aim was to assess the variation of organic matter (OM) composition, production, distribution and fate associated with coastal and southern mixed regions and also the summer stratified region. Based on the distribution of a suite of diagnostic biomarkers, including phospholipid fatty acids, source-specific sterols, wax esters and C25 highly branched isoprenoids, diatoms, dinoflagellates and green algae were identified as major contributors of marine organic matter (MOM) in this setting. The distribution of cholesterol, wax esters and C20 and C22 polyunsaturated fatty acids indicate that copepod grazing represents an important process for mineralising this primary production. Net tow data from 2010 revealed much greater phytoplankton and zooplankton biomass in well-mixed waters compared to stratified waters. This appears to be largely reflected in MOM input to surface sediments. Terrestrial organic matter (TOM), derived from higher plants, was identified as a major source of OM regionally, but was concentrated in proximity to major riverine input at the Boyne Estuary and Dundalk Bay. Near-bottom residual circulation and the seasonal gyre also likely play a role in the fate of TOM in the western Irish Sea.
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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 | 0.001 |
| 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) | 0.001 | 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".