Will understanding the ocean lead to “the ocean we want”?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The United Nations Decade of Ocean Science for Sustainable Development (2021–2030, henceforth the Ocean Decade) aims to galvanize the international community to acquire and apply scientific knowledge of the ocean. The Ocean Decade is specifically intended to help achieve the Sustainable Development Goals (SDGs), including its promise to “leave no one behind,” which includes coastal Least Developed Countries and Small Island Developing States, and will undoubtedly influence research agendas and financing well beyond 2030. This focus is captured in the phrase “the science we need for the ocean we want” (1). This first-of-its-kind UN Decade will require ambition and commitment, especially during the coronavirus disease 2019 (COVID-19) crisis. Researchers hoping to help deliver the “ocean we want” as a society-first principle need to understand how science can benefit ocean-dependent people. This requires a science model that co-designs and co-delivers solutions in collaboration with people whose livelihood depends on the ocean, such as the Madagascar fishers pictured here. Image credit: ©artush/123RF.com. The current draft of the Ocean Decade Implementation Plan establishes a framework of outcomes, actions, and objectives, acknowledging the need for interdisciplinary approaches to design and deliver solution-oriented research alongside ocean-dependent people (1). Recent proposals from the academic literature for the Ocean Decade emphasize increasing our global biophysical understanding through exploration and observation of, and experimentation on, the ocean (2⇓⇓–5). But will understanding the ocean lead to “the ocean we want”? We argue that proposals for the UN Decade should consider a crucial point: To achieve the ocean we want, we must better understand the needs and priorities of ocean-dependent peoples and evaluate potential solutions for them. Advancements in marine scientific knowledge and technological innovation have brought myriad benefits to people and the planet. They include: understanding global environmental change; assessing effects of anthropogenic … [↵][1]1To whom correspondence may be addressed. Email: geralds{at}mun.ca. [1]: #xref-corresp-1-1
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it