High intraspecific variation in fatty acids of<i>Eudiaptomus</i>in boreal and subarctic lakes
Bibliographic record
Abstract
The fatty acid (FA) composition of zooplankton has taxon-specific characteristics but may also be influenced by various environmental factors. Abiotic properties of lakes (location, morphometry, water chemistry and temperature) shape the phytoplankton community structure. We studied how this may be manifested in the FA composition of the common freshwater calanoid copepod Eudiaptomus spp. The proportions of saturated and polyunsaturated FA in Eudiaptomus, sampled from 25 lakes in boreal and subarctic regions, showed large variation (ca. 20–60 and 30–70%, respectively), while the proportion of monounsaturated FA was less variable (5–15%). The FA composition of Eudiaptomus differed significantly between subarctic and boreal lakes. Eudiaptomus from subarctic lakes had a higher proportion of 22:6ω3 and lower proportions of 18:2ω6, 18:3ω3 and 20:5ω3 than Eudiaptomus from boreal lakes. In the boreal lakes, the ω3:ω6 ratio in Eudiaptomus increased with increasing nutrients, chl a and dissolved organic carbon, presumably due to parallel changes in phytoplankton community composition, but only in the large and clearwater lakes, and not in the small and humic ones. Copepods are seasonally important prey for planktivorous fish, and the observed among-lake differences in the proportions of essential FA may influence fish growth and reproduction.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".