Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example
Bibliographic record
Abstract
Abstract Between 2014 and 2016, an interdisciplinary team of researchers including physical oceanographers, biologists, economists and anthropologists developed a working example of an Integrated Ecosystem Assessment (IEA) for three ecologically distinct regions of the Northwest Atlantic; Georges Bank, the Gulf of Maine and the Grand Banks, as part of the International Council for the Exploration of the Sea (ICES) Working Group on the Northwest Atlantic Regional Sea (WGNARS). In this paper, we review the transdisciplinary and collaborative process by which the IEA was developed, with a particular focus on the decision points arising from the IEA construct itself. The aim is to identify key issues faced in developing any IEA, practical decisions made to address these issues within the working group and lessons learned from the process.
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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 | 0.133 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".