Effect of lipid extraction on analyses of stable carbon and stable nitrogen isotopes in coastal organisms of the Aleutian archipelago
Bibliographic record
Abstract
We tested whether extracting lipids reduced confounding variation in δ13C and δ15N values by analyzing paired lipid-extracted (LE) and non-lipid-extracted (NLE) samples of bald eagle ( Haliaeetus leucocephalus (L., 1766)) whole eggs, muscle tissue from nine seabird and one terrestrial bird species, muscle tissue from four marine fish species, and blue mussels ( Mytilus edulis L., 1758) collected from the Aleutian archipelago, Alaska. Lipid extraction significantly increased δ13C by an average of 2.0‰ in whole eggs, 0.8‰ in avian muscle, 0.2‰ in fish muscle, and 0.6‰ in blue mussels. Lower δ13C values in NLE samples covaried positively with lipid content across all sample types. Lower δ13C values in NLE samples were not correlated with lipid content within bald eagle eggs and blue mussels, but covaried positively with percent lipid in avian and fish muscles. Neither lipid extraction nor percent lipid significantly changed δ15N values for any sample type. Lower δ13C values in most NLE avian and fish muscle tissues should not confound interpretation of pelagic versus nearshore sources of primary production, but lipid extraction may be necessary when highly precise estimates of δ13C are needed. Lipid extraction may not be necessary when only δ15N is of interest.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".