Ship to shore: ROV dive logging and data acquisition
Bibliographic record
Abstract
The Observatory Operations Division at Ocean Networks Canada (ONC) is responsible for the maintenance and operation of the entire digital and marine infrastructure across the VENUS, NEPTUNE, and Cambridge Bay facilities. Depending on planned work, environment and scheduling limitations, various ships and Remotely Operated Vehicles (ROVs) are contracted for the numerous expeditions conducted each year. For instance, during August and September 2015, ONC will be conducting four major expeditions concurrently with multiple ships (EV Nautilus, RV Thomas G. Thompson) and ROVs (Hercules, Argus, Jason/Medea) in the North East Pacific, divers in Cambridge Bay, and a 24 hour shore support centre. This paper focusses on the enabling digital infrastructure that is serving to streamline ROV data acquisition, enhance shore support and multi-vessel expeditions, and enable derivative applications and outreach initiatives. ROV instrumentation commonly includes video cameras, digital still cameras, navigational sensors, scientific sensors (e.g., CTDs, oxygen sensors) and sonar. Leveraging high-bandwidth satellite communications, ONC has developed a ship-based version of Oceans 2.0 that interfaces with selected ROV instrumentation and data streams for data collection, parsing and viewing purposes on the ship. It also transmits that data to shore where it is automatically integrated into the main ONC data repository and instantly available to anyone via ONC's data distribution platforms. High resolution video is typically archived on the ship (and archived on shore post-cruise), while lower resolution video is streamed to shore to be recorded, archived and made available through SeaTube in Oceans 2.0. In the last 2 years, ONC has started to incorporate audio into the video streams and recordings that capture the dialog between ROV operators, ONC staff and other participants. This networked ship to shore data acquisition approach streamlines the data archival process and allows shore-based personnel and scientists to engage in cruise activities. In conjunction with specialized Oceans 2.0 modules and an audio communication system, these high-bandwidth communications and transmitted data streams also enhance shore support capabilities. ONC has recognized this as an opportunity to conduct dive logging operations from shore and to include participants whose guidance is needed for specific operations, reducing the burden of limited ship berths. The two-way audio communication system permits dive loggers to engage with ROV operators and the dive chief almost as if they were on the ship. The SeaScribe dive logging annotation module, first developed by ONC in 2013 and presented at Oceans '13 in San Diego, has recently been extended to support annotations on up to 10 servers on multiple vessels simultaneously. Annotations are synced from one server to another whenever communications permit. Other features to improve usability and functionality have also been added since its initial conception. Shore support duties like systems operations, data stewardship, data commissioning, and public engagement also benefit from these enabling technologies. Derivative applications and outreach initiatives demonstrate further advantages from the integrated data and dive log annotations. Examples of derivative applications include the Oceans 2.0 SeaTube module, a Marine Protected Areas geodatabase for Endeavour Ridge, and scientific analysis on visual survey transects. These applications have in turn driven improvements to the dive log annotation syntax, filtering and export options. Outreach initiatives surrounding expeditions, such as the Wiring the Abyss website, feature live feeds of the video, audio and dive logs. Video streams from webcams showing on-deck and lab activities are also incorporated. The level of coordination between shore personnel and multiple ship/ROV crews would not be possible without this sophisticated digital infrastructure. Further intended improvements include data acquisition support for additional ROV instrumentation, more automated video transcoding and integration, and better support for recording deck operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.019 |
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".