Structural uncertainty in qualitative models for ecosystem-based management of Georges Bank
Bibliographic record
Abstract
Quantitative models for marine ecosystem-based management are often constrained by availability of observations. Uncertainty about the underlying system structure can affect model estimates and conclusions about the consequences of management actions. Qualitative models can augment model development for decision-making and may provide an alternative to quantitative assessments. We apply qualitative loop analysis to assess the sensitivity of management outcomes to structural uncertainty within the Georges Bank social–ecological system. Loop analysis uses defined positive or negative relationships between system components to provide inference about cascading effects of pressures on components of management interest. We compare the sensitivity of outcomes from two management strategies in four model structures of the Georges Bank system that investigate trophic and socioeconomic model uncertainty. We summarize system responses to perturbation and compare these responses with a set of management objectives. Models with complex socioeconomic structure estimated positive outcomes more often but with less reliability than simpler models. Our analyses demonstrated trade-offs among habitat objectives for two management strategies, as well as uncertainty about the reliability of outcomes, contingent on model structure.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".