Bibliographic record
Abstract
In May of 2011, the Canadian Ice Service, funded their first ever under-iceberg AUV-based mapping initiative. The Canadian Ice Service’s mission is to provide the most timely and accurate information on ice conditions in Canada’s navigable waters. The service provides radar imagery, ice bulletins, tracking of icebergs, modeling, ice coverage graphs and a host of other products to support activities such as tourism, shipping, commercial fishing and the oil and gas industry. The goal established was to map the same iceberg twice – early and again late in the summer season. Calculations of ice volume and roughness from the data would help forecast models of iceberg melt and scour. The team embarked aboard the Canadian Icebreaker Amundsen on July 18, mobilizing the entire effort in just over 2 months. The AUV was operated inverted, to allow for proper orientation of the sonar, and on a tether to allow for recovery of the AUV in the event of mission abort. To augment the AUV data, the University of New Brunswick, was enlisted to operate an EM3002 with a 40-degree side mount. This system was used to map the periphery of the berg prior to AUV operations. In addition ground penetrating radar measurements were made of the thickness of the iceberg from its surface. The iceberg found, in the truncated window allotted for search, was far larger and deeper than could be practiced during shakedown operations. Sadly, the depths required for operation, exceeding 120 m, were too deep for AUV operations with the tether. A frustrating 8 hours of attempts were executed with little data collected. None-the-less, the EM3002 dataset and GPR measurements, combined with aerial photography and the choice of a tabular iceberg provided an adequate data set for volume and roughness estimates. The iceberg was tracked with GPS/Iridium beacons through July and August. It disintegrated almost entirely in September preventing its revisiting in October. The Canadian Ice Service enlisted the AUV team to attempt a second go at under-iceberg mapping in October to resolve issues from the July cruise leg. This mission was far more successful and proved quite promising for future attempts. Presenter Bio Val Schmidt is pursuing a Ph.D. in Ocean Engineering under professors Chris d'Moustier and Larry Mayer. Before coming to CCOM, Val served in the US Submarine Service aboard the USS HAWKBILL (SSN-666). In this capacity, Val participated in two dedicated science ("SCICEX") missions to the Arctic in 1998 and 1999. He has since worked for Qwest Communications developing applications for Voice Over IP technologies and more recently as a research engineer for the Lamont-Doherty Earth Observatory where he built instrumentation, installed navigation and communications systems, and designed data systems for oceanographic research ships. Val is the lead author of the MB-System Cookbook. The Cookbook provides documentation for the open source swath mapping data processing suite. He also co-developed SWAP - the "Ship-to-Ship Wireless Access Protocol" - a wireless mesh-networking system for the UNOLS oceanographic research fleet. Val has a terrific and beautiful wife, Alice and a mean cat, Kokua.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".