Seasonality, littoral versus pelagic carbon sources, and stepwise <sup>15</sup>N-enrichment of pelagic food web in a deep subalpine lake: the role of planktivorous fish
Bibliographic record
Abstract
While the role of littoral food sources in shallow lakes has been widely investigated, uncertainties still exist about the relevance of such sources for deep lakes. Here we report quantitative estimates for the contribution of littoral versus pelagic sources in supporting the three most important planktivorous fish of a deep, large, subalpine lake in Italy. Contributions of pelagic (p) and littoral (q) signatures of δ13C and δ15N stable isotopes were detected in fish muscular tissue by applying a dynamic baseline mixing model. This model integrates tissue-specific metabolic turnover (m) and fish growth (k) rates over baselines δ13C seasonality. Annual fluctuations for both pelagic and littoral baselines were not negligible (ΔC = 10‰ and 8‰, respectively). We calculated that they could not be ignored, since contributions of pelagic and littoral signatures would be largely underestimated (up to 30% p and 13% q for whitefish (Coregonus lavaretus) and roach (Rutilus rutilus), respectively). When fish relied upon pelagic consumers, stepwise 15N-enrichment (E) of pelagic preys linearly decreased with prey-size-specific predation pressure. Therefore, longer food webs would be proportionally less stepwise 15N-enriched than shorter ones.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".