Estimation of predator-prey mass ratios using stable isotopes: sources of errors
Bibliographic record
Abstract
In aquatic systems, the ratio of predator mass to prey mass (PPMR) is an important constraint on food web structure, and has been correlated with environmental stability. One common approach of estimating PPMR uses nitrogen stable isotopes (δ15N) as an indicator of trophic position, under the assumption that the discrimination between diet and tissue is constant with increasing diet δ15N (an additive approach). However, recent studies have shown that this assumption may not be valid, and that there is a negative trend between the δ15N of the diet and the discrimination value (a scaled approach). We estimated PPMR for a simulated food web using the traditional additive approach and improved scaled approach, before testing our predictions with isotope samples from a North Sea food web. Our simulations show that the additive approach gives incorrect estimates of PPMR, and these biases are reflected in North Sea PPMR estimates. The extent of the bias is dependent on the baseline δ15N and trophic level sampled, with the greatest differences for samples with low baseline δ15N sampled at lower trophic levels. The scaled approach allows for the comparison of PPMR across varying δ15N baselines and trophic levels, and will refine estimates of PPMR.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".