Bibliographic record
Abstract
Underwater vehicle development in Canada has been underway for more than 40 years. Most of this work has been carried out in British Columbia. The developments include Manned Submersibles, ROVs and AUVs. The user of these vehicles includes offshore petroleum, telephone cable maintenance, science, surveying, salvage, and military. The enabling technologies for these integrations are mature. Today, some services may not be provided by relevant agencies with marine missions worldwide as they are short of the funding needed to discharge duties. There is the constant lament that there are not enough ships, people, or hardware. Perhaps if we are smart enough we can accomplish a portion of these tasks using advanced technology which may be considered today to be unconventional wisdom - namely robotics. Some may observe that many types of underwater vehicles already exist, or at least subsets exist. Therefore, some might wonder if they are any good and why are not more people using them? The reasons are that there is confusion regarding where to establish the boundary conditions for proper comparisons of performance. Also, all the potential vehicle integrations have not been fielded. An important aspect of acceptance limiting the use of vehicles is represented by the changes required in personnel establishment and training. We are in a period of transition and in these transitional periods false starts can be expected as the vision of the customer and the supplier is not necessarily clear because of lack of hands on experience. We sometimes see this lack of experience manifested in specifications which describe impossible-to-build vehicles. This paper presents the use of existing integrations which have contributed to the development of hybrid vehicles. This contributes to the capability to integrate systems to acquire the data to support the acquisition of data for the submission to the Internal Sea Bed Authority in accordance with Article 76. Article 76 provides instructions regarding how coastal states to exercise sovereign rights beyond the customary 200 nautical mile limit. The procedures for defining the outer limits of extended jurisdiction are based upon bathymetric and geological criteria. The procedures impose requirements to assemble, manipulate, visualize, and analyze a wide range of information in an accurate and well-documented fashion that is consistent with the reporting requirements of the International Seabed Authority. Underwater vehicles will be used to obtain some of the information required.
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 | 0.000 | 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".