Cabled observing stations for remote locations
Bibliographic record
Abstract
Using experience and technologies developed for Ocean Networks Canada's (ONC) seafloor cabled networks we have developed a small scale observing station that can be easily deployed in remote locations such as Canada's high Arctic. These areas provide challenges in a number of areas; environmental, logistic and social. In this paper we will discuss how these challenges are met and some strategies that we use to help ensure successful deployments of these stations. The engineering of these projects required new technology to be developed to deal with the different environments. In some respects things are easier, the platforms currently deployed and planned for future deployment are all in shallow water and in seismically quiet zones. However, the remote location and extreme weather conditions in the Arctic provide new challenges. There are three main components to these system: the underwater platform connecting all the instruments to the relay station on shore that includes a weather station and camera and transmits all the data to the shore station that buffers and forwards data to a data centre. The main challenge for the underwater component is to ensure that the ice does not interfere with the systems. The land-based relay tower uses wireless networking components that are vulnerable to environmental damage and vandalism. The final transmission of data from the shore station to the data centre is done through a commercial satellite-based channel that is also subject to weather and other environmental challenges, has relatively low bandwidth and is not 100% reliable. The remote observing stations were designed to be relatively small and lightweight so that one or two people can easily deploy and recover the main sensor tripod using only a small boat and a diver. The major engineering obstacles for the arctic deployment were the extreme low temperatures experienced during the winter months and the formation (and especially the subsequent break-up) of surface sea-ice. The relay station is perhaps the most vulnerable link in the whole system. Data are generally not buffered before reaching the shore station so any failure of the system prior to that will result in data loss. The relay component is located beside the water and is more exposed to the elements. Once the data reach the shore station, servers buffer the data. Buffering protects data against network outages and allows large data sets such as video data to be sent later at a reduced bandwidth. Logistics requires careful planning as only limited supplies are available on site. Keeping the platforms relatively small not only reduces the overall cost of deployment and general maintenance but also allows for a greater amount of community involvement and personal interaction. Involving the community in the project significantly increases chances of success. To date Ocean Networks Canada has deployed two observing stations, one at Brentwood College School (BCS) near Victoria, BC and one in Cambridge Bay, Nunavut, in Canada's high Arctic. The first installation, BCS, was a proof of concept and served as a test for the second installation in a remote location. More installations are being planned and refinements are in the works to improve the system. Using copper cable for data transmission limits the length of cable that can be used, this limit can be greatly extended by using fibre optic cables for data transmission. ONC has also designed new underwater interface cans to connect instruments to shore. These new devices will have the ability to support more instruments and provide better monitoring and control. They also allow greater flexibility in the variety of instruments that can be connected to the observing stations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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 source (direct Gemma or distilled Codex), 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".