2021004PGC cruise report: mapping Salish Sea marine geohazards, British Columbia
Bibliographic record
Abstract
The Geological Survey of Canada (GSC) undertook marine fieldwork onboard the Canadian Coast Guard Ship (CCGS) Vector to locate and map potential geohazards and geological features in the Salish Sea in the interest of public safety from August 11-18, 2021. This work was conducted under the Natural Resources Canada Marine Geoscience for Marine Spatial Planning (MGMSP) and the Public Safety Geoscience Programs. The GSC had observed multiple potential faults in existing data near Central Haro Strait, Stuart Channel, South of Hornby Island and near Cape Lazo through existing CHIRP and multibeam bathymetry data but required further data to quantify their activity and potential seismic risk (Barrie et al, 2021). In addition to fault activity, the GSC had detected numerous large underwater landslide deposits in Howe Sound and Saanich Inlet. The GSC required further data to constrain volumes and timing of slide activity. In English Bay the origin and evolution of a field of pockmarks was poorly understood. In Burrard Inlet, the survey required a better understanding of frequency of landslides as well as depth of sediment in order to understand natural sediment depositional rates. The research expedition included deep-tow system (DTS) sub-bottom surveys and multibeam water column and bathymetric surveys in each of these areas to better understand these marine geohazards and processes. Hydrographic surveys were completed by the Canadian Hydrographic Service (CHS) at night in Pylades Channel and near Point Grey to maximize use of ship time. Weather was good, seas were calm, and good quality data were collected. The data collected will be made publicly available and have the potential to contribute to building codes and to help communities in their decision-making and understanding of risks.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".