Bibliographic record
Abstract
One of the objectives of the NEPTUNE Canada Regional Cable Observatory (RCO) is to provide a communications platform for undersea applications linked to the global Internet. Supported applications include sensor polling, command and control, file transfer, data streaming and video streaming. Ideally, the observatory infrastructure will also be forward compatible with as yet unknown applications. Achieving these goals requires a comprehensive communications architecture stretching from the most remote instruments to the Internet. The key components of this architecture are the observatory backbone and nodes, extension cables, junction boxes, serial device servers, shore based routers, and a backhaul link connecting the shore station to the Internet. The observatory backbone and nodes are integrated and delivered under a single supply contract. Junction boxes, extension cables, instrument pods and the science instruments and sensors are each procured separately and must be integrated and tested prior to deployment. The IEEE 802.1 and 802.3 (Ethernet) series of standards are relied upon to ensure compatibility between network elements. Link Aggregation Control Protocol (802.3ad) and Virtual Local Area Networking (802.1Q) provide essential functionality for observatory operation. The NEPTUNE Canada communications architecture immediately supports IPv4 and key underwater components also support IPv6. The NEPTUNE Canada design allows expansion through the addition of nodes and junction boxes, upgrade from 2.5 Gb/s to 10 Gb/s optical channels, and extension of the backbone cable. The network design selected by NEPTUNE Canada has many benefits, but is not ideal in all respects. Nonetheless, an analysis of the design risks and tradeoffs shows that most of these are well managed by the selected design. Future generations of RCOs can extend the NEPTUNE Canada architecture by increasing the number of optical fibers, increasing the optical channel data rate, or introducing node-to-node optical channels.
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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 | 0.000 | 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".