Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous underwater vehicles (AUV) are poised as a leading technology for performing ocean survey and data collection. This is especially true in ice covered regions. Operations in which an AUV must be deployed through a hole in the ice presents many unique challenges that differ from ship based operations where the AUV is fully accessible via open leads. Typically, significant infrastructure is required to launch and recover a vehicle through the ice. In order to minimize cost and effort, an in-water/though-ice docking system that enables AUV capture and restraint for charging, data upload, and navigational alignment has been designed, tested and successfully deployed. The Canadian AUV through-ice capture and hold system (CATCHY) developed by Memorial University for Natural Resources Canada under Project CORNERSTONE is described in this paper. This system utilizes a robust mechanical system in conjunction with an auxiliary remotely operated vehicle (ROV) through a set of operational procedures. This system has been tested successfully, both in a controlled tank environment and through a field deployment in the Arctic. Lessons learned in this development will be utilized for future on ice AUV work.
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.
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 it