Caution on isotopic model use for analyses of consumer diet
Bibliographic record
Abstract
Isotopic models are increasingly used to determine the relative contribution of different food sources to an animal’s diet. However, these models are based on restrictive assumptions and provide estimates rather than exact values of contributions to consumer diet. The sources of inaccuracies in isotopic models are not well understood and laboratory experiments may be useful to evaluate model performance. In this paper we assess the accuracy of the three main isotopic models in controlled laboratory experiments, involving a breed of Norway rats ( Rattus norvegicus (Berkenhout, 1769)), in which the isotopic values of resources are known. At the same time, we measure errors resulting from the use of fixed or specific discrimination factor values for each resource and tissue. We show that the results of the three main isotopic models deviate considerably from the correct values in some cases. Estimations obtained using specific discrimination factors (corresponding to each experimental diet and tissue) were more accurate than those obtained using fixed discrimination factors (obtained from the literature). In addition, estimations varied depending on the tissue used, with the liver giving more accurate results than muscle or hair. We discuss the assumptions and limitations of isotopic models and highlight the importance of taking these assumptions into account when the most accurate results are sought. Finally, we propose some recommendations for the correct use of isotopic models, emphasizing the need to use specific discrimination factors for each species, tissue, and diet isotopic value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".