Mobile access to the NEPTUNE Canada project: Reuniting scientists with their instruments
Bibliographic record
Abstract
The NEPTUNE Canada project (North-East Pacific Time-series Undersea Networked Experiments), has laid an 800 km network of electro-optic cable on the seabed over the northern part of the Juan de Fuca tectonic plate, a 200,000 sq km region in the northeast Pacific off British Columbia, Washington and Oregon. The NEPTUNE Canada cable network currently features five seafloor laboratories called nodes. Through these nodes, land-based scientists control and monitor sampling instruments, video cameras, and remote operated vehicles as they collect data from the ocean surface to beneath the seafloor. NEPTUNE Canada provides data streams and notification alerts accessible through the web and scientists can browse the constant stream of data using the web tools to detect anomalies or events of interest. While this setup provides excellent access to data, mechanisms for receiving automatic notifications are still being improved. The introduction of smart phones allows for convenient access to the internet. As the data streams at NEPTUNE Canada are available online, access is also available through a smart phone. However, this mobile technology allows for more than just access: it facilitates the notifications to occur anywhere at any time, and provides a ready interface for response. NEPTUNE Canada is currently researching and developing mobile phone applications, taking advantage of mobile access and real-time notification. The strength of a mobile application lies in its pervasiveness of access. A scientist may be in their lab, in the field, or at a conference, and can be connected to their data. It is possible to receive customized notifications, such as detection of a dramatic change on a particular sensor. A notification can alert a scientist when such an event occurs, and they would be able to respond accordingly, for example, by increasing the sampling rate of the sensor or by examining additional sensors at other locations. The automatic detection and notification of events would more closely unite the scientist with their instruments by providing infrastructure for immediate feedback and response.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".