Critical Infrastructure for Ocean Research and Societal Needs in 2030
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 States has jurisdiction over 3.4 million square miles of ocean expanse greater than the land area of all fifty states combined. This vast marine area offers researchers opportunities to investigate the ocean's role in an integrated Earth system, but also presents challenges to society, including damaging tsunamis and hurricanes, industrial accidents, and outbreaks of waterborne diseases. The 2010 Gulf of Mexico Deepwater Horizon oil spill and 2011 Japanese earthquake and tsunami are vivid reminders that a broad range of infrastructure is needed to advance our still-incomplete understanding of the ocean. The National Research Council (NRC)'s Ocean Studies Board was asked by the National Science and Technology Council's Subcommittee on Ocean Science and Technology, comprised of 25 U.S. government agencies, to examine infrastructure needs for ocean research in the year 2030. This request reflects concern, among a myriad of marine issues, over the present state of aging and obsolete infrastructure, insufficient capacity, growing technological gaps, and declining national leadership in marine technological development; issues brought to the nation's attention in 2004 by the U.S. Commission on Ocean Policy. A 15-member committee of experts identified four themes that encompass 32 future ocean research questions enabling stewardship of the environment, protecting life and property, promoting economic vitality, and increasing fundamental scientific understanding. Many of the questions in the report (e.g., sea level rise, sustainable fisheries, the global water cycle) reflect challenging, multidisciplinary science questions that are clearly relevant today, and are likely to take decades of effort to solve. As such, U.S. ocean research will require a growing suite of ocean infrastructure for a range of activities, such as high quality, sustained time series observations or autonomous monitoring at a broad range of spatial and temporal scales. Consequently, a coordinated national plan for making future strategic investments becomes an imperative to address societal needs. Such a plan should be based upon known priorities and should be reviewed every 5-10 years to optimize the federal investment. The committee examined the past 20 years of technological advances and ocean infrastructure investments (such as the rise in use of self-propelled, uncrewed, underwater autonomous vehicles), assessed infrastructure that would be required to address future ocean research questions, and characterized ocean infrastructure trends for 2030. One conclusion was that ships will continue to be essential, especially because they provide a platform for enabling other infrastructure autonomous and remotely operated vehicles; samplers and sensors; moorings and cabled systems; and perhaps most importantly, the human assets of scientists, technical staff, and students. A comprehensive, long-term research fleet plan should be implemented in order to retain access to the sea. The current report also calls for continuing U.S. capability to access fully and partially ice-covered seas; supporting innovation, particularly the development of biogeochemical sensors; enhancing computing and modeling capacity and capability; establishing broadly accessible data management facilities; and increasing interdisciplinary education and promoting a technically-skilled workforce. The committee also provided a framework for prioritizing future investment in ocean infrastructure. They recommend that development, maintenance, or replacement of ocean research infrastructure assets should be prioritized in terms of societal benefit, with particular consideration given to usefulness for addressing important science questions; affordability, efficiency, and longevity; and ability to contribute to other missions or applications. These criteria are the foundation for prioritizing ocean research infrastructure investments by estimating the economic costs and benefits of each potential infrastructure investment, and funding those investments that collectively produce the largest expected net benefit over time. While this type of process is clearly subject to budget constraints, it could quantify the often informal evaluation of linkages between infrastructure, ocean research, the value of information produced, societal objectives, and economic benefits. Addressing the numerous complex science questions facing the entire ocean research enterprise in 2030 from government to academia, industry to nonprofits, local to global scale represents a major challenge, requiring collaboration across the breadth of the ocean sciences community and nearly seamless coordination between ocean-related federal agencies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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