Cabled Community Observatories for Coastal Monitoring - Developing Priorities and Comparing Results
Bibliographic record
Abstract
Coastal marine environments are some of the most bio-rich ecosystems on earth, providing food and livelihoods for many coastal community members. Yet, many of these coastal environments are threatened by anthropogenic and climate stressors resulting in the need for robust monitoring to inform current and future environmental stewardship decisions. While traditional oceanographic sampling is typically done from a research vessel, this is often costly and logistically difficult for scientists and coastal communities alike. Ocean Networks Canada (ONC) in partnership with communities along Canada's western, eastern, and Arctic coasts have continued to advance a number of solutions for long-term marine monitoring. Here, we discuss the development and implementation of seafloor cabled observatories, which collect continuous real-time data intended to help inform government, industry, and communities, and advance the scientific understanding of coastal environments. The strategic placement of these observatories, with collaboration from community partners, allows for high resolution datasets in both significant and remote regions of Canada. Each underwater platform has the potential to house a variety of instruments, which are connected to an underwater cable for real-time data transmission to the surface and which often allows the instruments to be powered from shore. Shore stations provide connectivity to the instruments and also provide an opportunity to host other shore-based instruments that can complement the underwater suite. Since large amounts of data are more easily and efficiently stored and transferred continuously rather than with battery-dependent autonomous systems, these observatory systems are well suited for collecting year-round, high temporal-resolution data. Thisallows for traditional physical oceanographic parameters to reveal environmental changes not only over annual and decadal time scales but also over diurnal (and shorter) time scales. These systems are more efficient at supporting year-round deployments of hydrophones, active acoustic instruments, surface and subsea video cameras, radars, and other high data-density instruments than their autonomous counterparts. Data from ONC's network of observatories are relayed from shore stations over the internet via fibre, cellular, or satellite connection to the Oceans 2.0 data management system. Oceans 2.0 supports open data access, detailed metadata support, sensor health monitoring, QA/QC of the data, data products, and a wide range of web services. All of these factors together serve to fill several major data and knowledge gaps to better understand our dynamic coastal waters. These community-based platforms have proven to be excellent tools for advancing and supporting ocean literacy and education resources by providing valuable and engaging resources to educators around the world, for all grade levels from primary education to postgraduate studies. They have also been used successfully as technology incubators where manufacturers and research groups can trial new sensor technologies in a real-world setting that is relatively easy to access and where they can leverage the continuous, real-time data stream and sensor-health monitoring that allows for instantaneous feedback on sensor performance. Finally, ONC's broad vision includes providing knowledge and leadership that deliver solutions to society in general; community-based cabled observatories are one of the means to support this vision. ONC's Community-Based Monitoring team usesthese cabled “community observatories” as one of many possible complementary tools when working directly with leadership in coastal communities, including Indigenous communities, environmental stewardship organizations, non-governmental organizations, and Municipal, Provincial, Federal government departments, and researchers, to implement local environmental monitoring programs. This paper will present a summary of multiple community observatory installations and will compare them to one another. It will discuss the various inputs and priorities that have influenced the development of each of the systems and how the data has been used in each scenario.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".