A Stable Isotope Analysis to Quantify the Contribution of Basal Dietary Sources to Food Webs of Drinking Water Reservoirs
Bibliographic record
Abstract
This study investigates the food web structure of the Xinlicheng Reservoir, a drinking water source of critical importance in Changchun, China, by employing stable isotope analysis (SIA) to quantify the contribution ratios of four basal dietary sources—phytoplankton, zooplankton, sediment organic matter, and particulate organic matter (POM)—to the diets of two key filter-feeding fish species, Hypophthalm ichthys molitrix and Aristichthys nobilis. The analysis reveals that phytoplankton is the dominant dietary source for both species, contributing 32.08% and 34.06%, respectively, whereas the POM contribution is discernably lower (13.25%). The average trophic level of the fish assemblage in Xinlicheng Reservoir is 3.03, while the trophic levels of the two filter-feeding species lie between 3.00 and 3.50. Furthermore, a random forest model was used to identify key environmental drivers of isotopic variations in these basal dietary sources, highlighting the significant role of pH, total nitrogen (TN), chloride (Cl−), calcium (Ca2+), phosphorus (TP), and silicate (SiO44−) in influencing carbon and nitrogen isotopic ratios. These findings provide critical insights to optimize biomanipulation strategies aimed at improving water quality in drinking water reservoirs by enhancing our understanding of the environmental factors that govern trophic interactions and broader food web dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".