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Record W4254595876 · doi:10.2172/1044991

Critical Infrastructure for Ocean Research and Societal Needs in 2030

2011· report· en· W4254595876 on OpenAlexfundno aff
National Research Council

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
FundersOffice of Naval ResearchOffice of ScienceU.S. Geological SurveyNational Oceanic and Atmospheric AdministrationHamilton Health Sciences FoundationBureau of Ocean Energy ManagementAustralian GovernmentCenters for Disease Control and PreventionNational Aeronautics and Space AdministrationU.S. Department of Homeland SecurityU.S. Department of JusticeU.S. Department of StateU.S. Department of TransportationUniversity of WashingtonNational Science FoundationNational Institutes of HealthU.S. Department of Health and Human ServicesU.S. Army Corps of EngineersU.S. Department of CommerceU.S. Department of EnergyU.S. Department of DefenseSmithsonian Institution
KeywordsOcean observationsPlan (archaeology)Multidisciplinary approachOcean scienceBusinessSuiteOrder (exchange)Environmental resource managementEnvironmental planningEnvironmental scienceOceanographyPolitical scienceGeographyMeteorologyFinance

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0130.015
Open science0.0020.013
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0240.007

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.

Opus teacher head0.077
GPT teacher head0.345
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations10
Published2011
Admission routes1
Has abstractyes

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