Influence of environmental conditions, population density, and prey type on the lipid content in Baltic herring (<i>Clupea harengus membras</i>) from the northern Baltic Sea
Bibliographic record
Abstract
Global climate change can affect the energy content of fish by altering their lipid physiology and consumption. We investigated the effects of different environmental stressors on the lipid content of the Baltic herring (Clupea harengus membras) from spawning ground samples that were collected annually in the northern Baltic Sea. During 1987–2014, the average lipid content of herring muscle decreased from 5%–6% (wet mass) to 1.5% (wet mass). Generalized linear mixed models indicated that sea water salinity and the size of the herring stock explained best the declining trend of lipid content. We estimated that the amount of the lipid storage incorporated in the spawning stock decreased by approximately 45% during the study, with respective energy content decreases. Fatty acid composition analysis revealed that herring lipids contained a high proportion of EPA (eicosapentaenoic acid; 20:5n-3) and DHA (docosahexaenoic acid; 22:6n-3), which likely originated from its main summertime prey, Limnocalanus macrurus. The results illustrate various climate change-induced processes leading to changes in the lipid content of the Baltic herring and, consequently, to changes in the energy flows of the northern Baltic ecosystem.
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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) | 0.000 | 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".