Description of the watershed outline and water depth survey datasets for Whale Cove, Nunavut
Bibliographic record
Abstract
A watershed outline map, a bathymetric map, and water related statistics of the Whale Cove water supply reservoirs where produced following field work performed during the summer of 2009 by researchers from Canada Centre for Remote Sensing in collaboration with staff from the Nunavut Research Institute. Since 2007, with support from Indian and Northern Affairs Canada (INAC), a small team of scientists from the Canada Centre for Remote Sensing were involved in a project to help characterize the water supply of Nunavut communities. This was a complex task involving the delineation of watersheds and estimation of the water volume of the supply lake for the community. To estimate this water volume, a bathymetric map was produced based on field surveys using a depth sounder equipped with a GPS. CCRS developed a low cost and easy to use technique to enable such depth surveys to be rapidly carried out. The technology transfer aspect of the activity is aimed to allow Nunavut professionals to produce lake depth maps with low cost and easy to use tools and software. During the course of this project, between 2007 and 2009, these initiatives allowed researchers, engineers, managers, planners and technical personnel to perform lake and watershed surveys of the communities of Iqaluit, Clyde River, Arviat and Whale Cove. This document describes the digital datasets acquired for Whale Cove and distributed to the Department of Community and Government Services, the Nunavut Research Institute, and the Department of Indian and Northern Affairs. The enclosed datasets were produced under the ''Enhancing Resilience in a Changing Climate Program'' of the Earth Sciences Sector, Natural Resources Canada. Several large format image maps were printed and distributed to the organisations identified above, and were also presented at several workshops.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".