A practical introduction to stable-isotope analysis for seabird biologists: Approaches, cautions and caveats
Bibliographic record
Abstract
Stable isotopes of carbon and nitrogen can provide valuable insight into seabird diet, but when interpreting results, seabird biologists need to recognize the many assumptions and caveats inherent in such analyses. Here, we summarize the most common limitations of stable-isotope analysis as applied to ecology (species-specific discrimination factors, within-system comparisons, prey sampling, changes in isotopic ratios over time and biological or physiological influences) in the context of seabird biology. Discrimination factors are species specific for both the consumer and the prey species, and yet these remain largely unquantified for seabirds. Absolute comparisons across systems are confounded by differences in the isotopic composition at the base of each food web, which ultimately determine consumer isotopic values. This understanding also applies to applications of stable isotopes to historical seabird diet reconstruction for which historical prey isotopic values are not available. Finally, species biology (e.g. foraging behaviour) and physiologic condition (e.g. level of nutritional stress) must be considered if isotopic values are to be interpreted accurately. Stable-isotope ecology is a powerful tool in seabird biology, but its usefulness is determined by the ability of scientists to interpret its results properly.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.036 |
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".