Integrating human dimensions in decadal-scale prediction for marine social–ecological systems: lighting the grey zone
Bibliographic record
Abstract
Abstract The dynamics of marine systems at decadal scales are notoriously hard to predict—hence references to this timescale as the “grey zone” for ocean prediction. Nevertheless, decadal-scale prediction is a rapidly developing field with an increasing number of applications to help guide ocean stewardship and sustainable use of marine environments. Such predictions can provide industry and managers with information more suited to support planning and management over strategic timeframes, as compared to seasonal forecasts or long-term (century-scale) predictions. The most significant advances in capability for decadal-scale prediction over recent years have been for ocean physics and biogeochemistry, with some notable advances in ecological prediction skill. In this paper, we argue that the process of “lighting the grey zone” by providing improved predictions at decadal scales should also focus on including human dimensions in prediction systems to better meet the needs and priorities of end users. Our paper reviews information needs for decision-making at decadal scales and assesses current capabilities for meeting these needs. We identify key gaps in current capabilities, including the particular challenge of integrating human elements into decadal prediction systems. We then suggest approaches for overcoming these challenges and gaps, highlighting the important role of co-production of tools and scenarios, to build trust and ensure uptake with end users of decadal prediction systems. We also highlight opportunities for combining narratives and quantitative predictions to better incorporate the human dimension in future efforts to light the grey zone of decadal-scale prediction.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".