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Record W2148739996 · doi:10.1109/oceans.2008.5152030

Retrospective and prospective views of the Ocean Observing System in the Gulf of Maine

2008· article· en· W2148739996 on OpenAlexaboutno aff
Neal R. Pettigrew, John P. Wallinga, L. J. Mangum, Francois Neville

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersOffice of Naval Research
KeywordsBuoyMeteorologyEnvironmental scienceOcean observationsWinter stormOcean currentSea stateSea surface temperatureOceanographyShoreStormGeographyGeology

Abstract

fetched live from OpenAlex

The Gulf of Maine Ocean Observing System (GoMOOS) is a comprehensive prototype integrated coastal ocean observing system that was established in the summer of 2001. Its current configuration includes eleven solar-powered buoy-monitored locations with physical and optical sensors, four shore-based long-range HF radar systems for surface current measurement, operational circulation and wave models, satellite observations, inshore nutrient monitoring, and hourly web-delivery of data. It serves a broad array of real-time oceanographic and marine meteorological data and data products to scientists, state and federal regulators, the National Weather Service, both the US and Canadian Coast Guards, the National Data Buoy Center, educators, regional natural- resource managers, the Gulf of Maine fishing and maritime industries, local airports and airlines, sailors, and the general public. The ocean observing system that can be thought of as consisting of four major subsystems: the data acquisition subsystem; the data handling, processing, and archiving subsystem; the system of numerical nowcast and forecast models; and a web-based data distribution/presentation subsystem. The Gulf of Maine is a harsh operational environment. Winter storms pose severe challenges including the build up of sea ice on buoy, its solar panels, and its meteorological sensors. In summer the productive waters of the gulf can present severe biofouling problems that affect the operation of optical sensors. Thus, the periods of most difficult field operations coincide with the periods of greatest data value in terms of marine safety, search and rescue, and the monitoring biological productivity. Never-the-less, the data returns for the GoMOOS ocean sensor array has averaged approximately 90% over the first seven years of operation. This unusually high rate of data return is due in large part to our operational six-month duty/maintenance cycle on all equipment. We have 22 buoys for 11 locations, and 22 complete sets of instrumentation that are rotated in and out of service on a six month schedule. The challenges of the Gulf of Maine physical environment were largely understood a priori, and were incorporated into the system design criteria and the blueprints for the service and maintenance protocols. However, there were unanticipated challenges in the funding process that have caused the greatest difficulties, and which continue to pose a serious threat to continued operation and success of this ocean observing system, as well as others observing systems in the United States. Funding for the system has been chronically short and subject to the unpredictable fluctuations of a US congressional appropriations process. The inadequacy and variability of funding has substantially hampered the operations of many of the Integrated Ocean Observing Systems (IOOS), including GoMOOS, and has hindered technological improvements. The funding crisis has deepened to the point that, unless this trend is quickly reversed, the number of monitoring stations will be substantially reduced and system will no longer be able to serve many of citizens, organizations, and agencies that have come to rely upon the data it currently provides. Ocean sensors, ocean platform technologies, and modeling and visualization techniques are in a period of rapid technical development. If stable funding can be achieved, the capabilities of operational ocean observing systems could increase dramatically over the next decade. Autonomous vehicles could become the fast response survey fleet of the IOOS, as well as taking on routine, sustained marine survey functions that are already prohibitively expensive to perform using ships, and will become even more costly as energy costs rise in the future. Practical autonomous vehicles will likely expand beyond gliders and AUVs to include surface sail vessels and energy-efficient autonomous aerial drones. The combination of time series measurements from profiling packages on buoy arrays with the repeatedly spatial surveys of the autonomous fleets will provide a new look at our coastal oceans that could transform coastal ocean science and management. Coupled with these new platform advances will be significant growth in the areas of real-time biochemical sensors, sensor miniaturization, high-speed data telemetry technologies, increases in onboard data processing capabilities, routine two-way communications with submerged sensors, and advances in data visualization techniques that will amount to virtual presence. The single greatest enabling factor of these broad improvements to ocean observing operations and science appears, at present, to be the establishment of an adequate, stable, and predictable funding process.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.192
Teacher spread0.176 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2008
Admission routes1
Has abstractyes

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