Incorporating stable isotopes into a multidisciplinary framework to improve data inference and their conservation and management application
Bibliographic record
Abstract
Through its ability to address complex ecological questions and the possibility of analysing large sample sizes to understand population‑level processes, the use of stable isotope analysis (δ13C and δ15N) has grown rapidly in recent years. Importantly, it is now becoming an accepted tool to derive data for conservation and management planning at the species, community and ecosystem levels. With this acceptance, however, the stable isotope research community faces new challenges to ensure that data are interpreted and presented effectively to maximise their potential for guiding management. We present a case study on stable isotope trends in the vertebrae of white sharks Carcharodon carcharias to show how multiple plausible explanations could be provided to explain the observed isotopic patterns, a point that is likely ubiquitous among isotope studies in ecology. Based on this, we promote that integrating stable isotope data in a multidisciplinary framework will generate the most reliable data for conservationists and resource managers. If this is not possible, we suggest that the isotope community should be more accepting of presenting multiple possible explanations for trends observed in data, rather than focusing on a single interpretation that could potentially misguide management.
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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 | 0.098 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".