Reconstructing trophic interactions as a tool for understanding and managing ecosystems: application to a shallow eutrophic lake
Bibliographic record
Abstract
Consequences of disturbance and human intervention on lake ecosystems are difficult to anticipate solely by intuition because of the complex interactions that characterize lake communities. Understanding how the structure of the interactions buffers or amplifies external impacts may have beneficial effects on lake management. In this paper, the food web of a moderately eutrophic lake (Lake Mosvatn, Norway) is reconstructed by using the effects of biomanipulation in combination with loop analysis, a qualitative algorithm. The outcome is a signed digraph that predicts changes in the level of the variables for input entering the system through any component. Model predictions explain the observed patterns of abundance, and this suggests that the graph is a plausible description of the main trophic interactions in Lake Mosvatn. As such, it is used as a predictive tool to discuss problems related to nutrient enrichment. When multiple causes are responsible for the observed effects, explaining their relative contribution to the net outcome is a difficult task. By discussing patterns of abundance observed in Lake Mosvatn as due to different inputs, this paper illustrates how qualitative predictions can help in this respect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".