Effects of Lipid Extraction and Lipid Normalization on Stable Carbon and Nitrogen Isotope Ratios in Double-Crested Cormorants: Implications for Food Web Studies
Bibliographic record
Abstract
Cormorants are desirable subjects for food web studies using stable isotopes (C and N) because of global fisheries conflicts, but no validated lipid-normalization procedures are currently available for any cormorant species. Accordingly, the effects of chloroform-methanol and petroleum-ether lipid extractions and three published lipid-normalization models on stable C and N isotope signatures in Double-crested Cormorant (Phalacrocorax auritus) muscle and liver tissues were investigated. The presence of lipids in cormorant muscle and liver decreased δ13C values by approximately 1–2‰, more so than has been reported in other birds. Cormorants showed large variation in the relationship between the C:N ratio of bulk tissue and the change in δ13C values after lipid extraction, violating a major assumption of published lipid-normalization models. Despite this violation, two of the three tested models performed reasonably well for correcting δ13C values. The circumstances under which these models might fail are unknown, so caution is warranted when applying them to new species. Petroleum-ether lipid extractions did not reduce the C:N ratio of tissue samples to those of pure proteins (4.0 or below; over half of the samples ranged from 4.38 to 5.27); thus, lipid extraction using chloroform-methanol is recommended to ensure the greatest accuracy of carbon isotope analyses of cormorant tissues.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".