The influence of environmental parameters on spatial variation in zoobenthic density and stable isotopes (<scp>δ<sup>13</sup>C</scp>, <scp>δ<sup>15</sup>N</scp>, and <scp>δ<sup>34</sup>S</scp>) within a large lake
Bibliographic record
Abstract
Abstract The use of baselines in stable isotope studies to interpret food web structure is essential, but baseline isotope values are often assumed to be spatially homogeneous, even in large aquatic ecosystems. To test this assumption in large lakes, we quantified spatial variation in δ13C, δ15N (deposit‐feeding Oligochaeta and filter‐feeding Dreissena spp.), and δ34S (Dreissena spp. only) and density in Lake Erie between 2014 and 2016. Lake Erie's three distinct basins differ in size, bathymetry, and nutrient loading, making it an excellent system for exploring spatial variation in stable isotopes of baseline organisms. Dreissena spp. densities were highest in the western and lowest in the seasonally hypoxic central basin, while Oligochaeta densities were relatively consistent throughout Lake Erie. Values of δ13C, δ15N, and δ34S exhibited distinct spatial trends that were not related to population densities but followed the west to east direction of water flow within the lake. For both taxa, δ13C was lower in the deeper, oligotrophic east basin than the shallow, mesotrophic west basin, and δ15N and δ34S increased from west to east. Spatial patterns of low δ34S in Dreissena spp. in the western and central basins were likely related to hypoxia, whereas patterns of δ15N in both taxa were probably related to the greater influence of agricultural land uses in the western basin. Spatial trends of stable isotopes in large lake zoobenthos are driven by complex interactions of environmental gradients, which could introduce bias in evaluations of trophic structures within aquatic ecosystems that use stable isotopes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".