An arithmetic correction for the effect of lipid on carbon stable isotope ratios in muscle and digestive glands of the American lobster ( <scp> <i>Homarus americanus</i> </scp> )
Bibliographic record
Abstract
Rationale Lipid correction models use elemental carbon‐to‐nitrogen ratios to estimate the effect of lipids on δ 13 C values and provide a fast and inexpensive alternative to chemically removing lipids. However, the performance of these models varies, especially in whole‐body invertebrate samples. The generation of tissue‐specific lipid correction models for American lobsters, both an ecologically and an economically important species in eastern North America, will aid ecological research of this species and our understanding of the function of these models in invertebrates. Method We determined the δ 13 C and δ 15 N values before and after lipid extraction in muscle and digestive glands of juvenile and adult lobster. We assessed the performance of four commonly used models (nonlinear, linear, natural logarithm (LN) and generalized linear model (GLM)) at estimating lipid‐free δ 13 C values based on the non‐lipid‐extracted δ 13 C values and elemental C:N ratios. The accuracy of model predictions was tested using paired t ‐tests, and the performance of the different models was compared using the Akaike information criterion score. Results Lipid correction models accurately estimated post‐lipid‐extraction δ 13 C values in both tissues. The nonlinear model was the least accurate for both tissues. In muscle, the three other models performed well, and in digestive glands, the LN model provided the most accurate estimates throughout the range of C:N values. In both tissues, the GLM estimates were not independent of the post‐lipid‐extraction δ 13 C values, thus reducing their transferability to other datasets. Conclusions Whereas previous work found that whole‐body models poorly estimated the effect of lipids in invertebrates, we show that tissue‐specific lipid correction models can generate accurate and precise estimates of lipid‐free δ 13 C values in lobster. We suggest that the tissue‐specific logarithmic models presented here are the preferred models for accounting for the effect of lipid on lobster isotope ratios.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".