Zooplankton, lipids and stable isotopes: importance of seasonal, latitudinal, and taxonomic differences
Bibliographic record
Abstract
We found considerable seasonal, latitudinal, and taxonomic differences in zooplankton lipid content and concurrent δ13C values of zooplankton. We collected cladoceran as well as cyclopoid and calanoid copepod zooplankton from boreal and subarctic lakes throughout a year, allowing us to study zooplankton likely subjected to different isotopic fractionation processes and with highly variable lipid contents. Considerable seasonal variation was observed in the difference between bulk and lipid-extracted zooplankton δ13C values, indicating that seasonally changing lipid content introduced notable variation in zooplankton δ13C values. The difference between bulk and lipid-extracted material was most amplified in lipid-rich subarctic zooplankton in winter, δ13C difference being >5 units. Significant differences were also observed among zooplankton taxa, with copepods showing a greater lipid impact on δ13C than cladocerans. Published lipid correction models failed to produce satisfying fits to our data, and considerable variation was left even after recalibrating the model parameters. This was likely due to taxonomic differences in lipid effects on δ13C values. We therefore produced separate mass balance-based lipid correction models for cladocerans and also cyclopoid and calanoid copepods. We conclude that arithmetic lipid correction models perform well with zooplankton samples, but taxonomic differences need to be considered.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".