A decision support tool for response to global change in marine systems: the <scp>IMBER</scp>‐<scp>ADA</scp>pT Framework
Bibliographic record
Abstract
Abstract Global change is occurring now, often with consequences far beyond those anticipated. Although there is a wide range of assessment approaches available to address‐specific aspects of global change, there is currently no framework to identify what governance responses have worked and where, what has facilitated change and what preventative options are possible. To respond to this need, we present an integrated assessment framework that builds on knowledge learned from past experience of responses to global change in marine systems, to enable decision‐makers, researchers, managers and local stakeholders to: (i) make decisions efficiently; (ii) triage and improve their responses; and (iii) evaluate where to most effectively allocate resources to reduce vulnerability and enhance resilience of coastal people. This integrated assessment framework, IMBER‐ADApT is intended to enable and enhance decision‐making through the development, a typology of case‐studies providing lessons on how the natural, social and governance systems respond to the challenges of global change. The typology is developed from a database of case‐studies detailing the systems affected by change, responses to change and, critically, an appraisal of these responses, generating knowledge‐based solutions that can be applied to other comparable situations. Fisheries, which suffer from multiple pressures, are the current focus of the proposed framework, but it could be applied to a wide range of global change issues. IMBER‐ADApT has the potential to contribute to timely, cost‐effective policy and governing decision‐making and response. It offers cross‐scale learning to help ameliorate, and eventually prevent, loss of livelihoods, food sources and habitat.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".