Ocean sustainability in the context of global change: Lessons learned from a large-scale ocean research project
Bibliographic record
Abstract
The UN Decade of Ocean Science for Sustainable Development (2021-2030) has catalyzed a renewed focus on the importance of transformative science in support of sustainable solutions to pressing climate and biodiversity challenges. How that transformative science can be best fostered requires further clarity, along with examples of insights from basic research and its application for transformative change. This synthesis paper outlines the key empirical contributions and the procedural, organizational and technical lessons learned from a 10-year, large-scale global ocean research project aimed at fostering integrated marine research and the development of ocean sustainability options within and across the natural and social sciences. The specific objective the Integrated Marine Biosphere Research (IMBeR) project is to understand, quantify and compare historic and present structure and functioning of linked ocean and human systems. Outcomes of this global research effort include: 1) better understanding and quantification of the state and variability of marine ecosystems; 2) improved scenarios, predictions and projections of future ocean-human systems at multiple scales; and 3) enhanced understanding of enabling conditions for sustainable ocean governance in the context of rapid change. Outcomes of this synthesise can inform other basic natural and interdisciplinary large-scale ocean research efforts, and further contribute to ongoing global efforts to foster transformative science that links people and oceans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".