Assessing the utility of C:N ratios for predicting lipid content in fishes
Bibliographic record
Abstract
Numerous researchers have attempted to find suitable proxies for the lipid content of fishes. Owing to the high carbon content of lipids, C:N ratios have been used as a predictor of lipid content both for the purposes of quantifying condition and for stable isotope analyses. Here we examine the utility of C:N ratios for predicting the lipid content within and among populations, and to validate commonly used published percent lipid – C:N ratio models. No common percent lipid – C:N ratio model was found to apply; instead, population-specific influences on lipid content were observed. Published lipid prediction models significantly underestimated lipid content, and often had worse prediction error than the error obtained by using measured mean lipids as the prediction for all samples. Maximum prediction error by population ranged from a low of 50.7% to a high of 65.0%. Our results provide no support for the idea that there is a predictable relationship between bulk C:N ratios and lipid content. We recommend that sample-specific relationships be developed in situations where lipid prediction is needed, rather than relying on published models.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".