Intra‐lake stable isotope ratio variation in selected fish species and their possible carbon sources in Lake Kyoga (Uganda): implications for aquatic food web studies
Bibliographic record
Abstract
Abstract The stable isotopes of nitrogen (δ15N) and carbon (δ13C) provide powerful tools for quantifying trophic relationships and carbon flow to consumers in food webs; however, the isotopic signatures of organisms vary within a lake. Assessment of carbon and nitrogen isotopic signatures in a suite of plants, invertebrates, and fishes in Lake Kyoga, indicated significant variation between two sites for δ13C (paired t = 6.305; df = 14, P < 0.001 and δ15N paired t = 1.292; df = 14; P < 0.05). The fish fauna in Bukungu was generally more 13C enriched (mean δ13C = –16.37 ± 1.64‰) than in Iyingo (mean δ13C = –20.80 ± 2.41‰) but more δ15N depleted (mean δ15N = 5.57 ± 0.71‰) than in Iyingo (mean δ15N = 6.92 ± 0.83‰). The simultaneous shifts in phytoplankton and consumer signatures confirmed phytoplankton as the major source of carbon for the food chain leading to fish. Limited sampling coverage within lakes may affect lake wide stable isotope signatures, and the same error is transferred into trophic position estimation. Consideration of potential intra‐lake spatial variability in isotope ratios and size is essential in evaluating the spatial and trophic structure of fish assemblages.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".