A practical guide on stable isotope analysis for cetacean research
Bibliographic record
Abstract
Abstract Trophic ecology information about cetaceans is essential to understand their role in ecosystem dynamics. Stable isotope analysis is a valuable complementary approach to conventional methods usually applied to the study of the foraging behavior of cetaceans because it provides dietary information over different time scales and can potentially use tissues archived in scientific collections. However, the considerable increase in stable isotope analysis by a growing number of cetacean research groups demands the use of proper protocols to ensure that accurate isotopic data are obtained. We provide a theoretical background of stable isotope analysis and its application to assess cetaceans‘ trophic ecology. We review the factors that can influence isotopic measurements and propose a practical guideline with suitable techniques for sample preparation of biological tissues to be employed by researchers to yield reliability in the interpretation of isotopic data. We summarized the main assumptions and inherent limitations that can lead to confounding interpretations of isotopic data, such as species‐ and tissue‐specific discrimination factors, temporal or spatial variation in prey, and baseline isotopic values in the context of cetacean ecology. Our detailed review offers important guidance for researchers who want to use stable isotope analysis to address different ecological questions with cetacean species.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.047 |
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".