Lessons learned from the PICES FUTURE Program on development of an interdisciplinary international science program to advance ocean sustainability
Bibliographic record
Abstract
Abstract Interdisciplinary international science programs that combine environmental, ecological, and social research are pivotal in advancing ocean sustainability by integrating diverse expertise and fostering collaboration across borders. We examine the evolution and accomplishments of the North Pacific Marine Science Organization’s (PICES) Forecasting and Understanding Trends, Uncertainty and Responses of North Pacific Ecosystems (FUTURE) Program, designed to understand and communicate the future of North Pacific ecosystems under various natural and anthropogenic forces. The program’s unique application of the North Pacific Social-Ecological-Environmental Systems (SEES) framework has aimed to facilitate interdisciplinary collaboration and enhance our comprehension of ecosystem responses to climate variability. Through a combination of systematic review and quantitative text analysis of research outputs, we evaluate the program’s success in addressing its scientific objectives, and identify key areas for future research. Our findings highlight significant shifts in PICES’ research focus over time, evolving from basic marine science to applied ecosystem management. We also discuss the challenges faced in understanding ecosystem resilience, the impact of human activities, and the effectiveness of interdisciplinary approaches in advancing ocean sustainability. The lessons learned from the first two phases of the FUTURE Program (2010–2020) provide valuable insights for planning and executing large-scale international science initiatives aimed at enhancing ocean sustainability and addressing global climate variability.
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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 | 0.145 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".