Combined analyses reveal environmentally driven changes in the North Sea ecosystem and raise questions regarding what makes an ecosystem model’s performance credible?
Bibliographic record
Abstract
When an ecosystem model of the North Sea is calibrated to data from multiple trophic levels, the model estimated the primary production required to support the food web correlates temporally with observed changes in sea temperature and nutrient levels, supporting evidence from empirical analyses. However, a different result is given from an alternative calibration using fish stock data only. The inference taken from the emergent primary production – temperature relationship and empirical data are that, on balance, there is stronger overall evidence to support the calibration constrained at multiple trophic levels. Two important implications of the findings are (i) that the relative importance of fishing and environmental effects is likely to be interpreted differently depending on the calibration approach and (ii) the contrasting model calibrations would give different responses to fishing policies. It raises questions regarding how to judge the performance (and credibility) of an ecosystem model and the critical importance of conducting empirical and modelling analyses in parallel. Adopting a combined approach to ecosystem modelling is an important step in the pursuit of operational and defensible tools to support the ecosystem approach to management.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".