Use of stable isotopes in benthic organic material as a baseline for estimating fish trophic positions in lakes
Bibliographic record
Abstract
Estimating the trophic position of predators in food webs using stable isotopes requires establishing a “baseline”, typically served by bivalves or snails. However, the frequent absence of such organisms in lakes leaves researchers in a difficult situation. Here we test the hypothesis that trophic position (TP) of largemouth bass (LB; Micropterus salmoides) in lakes can be estimated from δ15N of benthic organic material (BOM) in littoral surficial sediment. δ15NLB as a function of δ15NBOM was significant across 51 lakes with an R2 of 0.84, supporting our hypothesis. In a subset of six lakes, the mean TPLB did not differ significantly when calculated using baselines of δ15N in BOM, unionid mussels (UN), gastropods (SN), or from a stable isotope mixing model. In a subset of 26 lakes, mean TPLB calculated from δ15NBOM and δ15NUN baselines were not significantly different, but TPLB calculated using δ15NBOM was not significantly correlated with TPLB calculated using δ15NUN. Although δ15NBOM should not be used interchangeably with δ15NUN as a baseline for estimating TPLB, δ15NBOM can be useful as a separate baseline where estimates of fish TP are needed in all study lakes given that mussels are frequently absent and BOM is always available for collection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".