Bathymetric mapping and monitoring for northern community impact assessment - Arviat, Nunavut
Bibliographic record
Abstract
This report highlights the Arviat water supply assessment activity conducted by scientists from the Canada Centre for Remote Sensing at Natural Resources Canada and staff from the Nunavut research Institute (NRI). This activity included the use of high resolution satellite imagery and on-site field surveys to map the lake depths of a lake close to Arviat and the acquisition of other geomatics datasets. This document is copyright of Natural Resources Canada and contains copyrighted material of Digital Globe Inc, the provider of the Quickbirdtm high resolution satellite image shown in this report. Digital computer files resulting from this project, and described in this document, are available upon request by contacting the project leader or project members. The digital files comprise of: - Raster files illustrating the water depth model of a lake close to Arviat (Ice Lake). (Geotiff.tif). - Vector files illustrating the depth contours (isobaths) of a lake close to Arviat (Ice Lake). (ESRI shapefile.shp). - Tabular statistics featuring the water volume of a lake close to Arviat (Ice Lake). - Vector files illustrating roads and trails (.shp) - Vector files illustrating the position of the water supply pipeline (.shp) - Vector files illustrating the joint position of the water pipeline (.shp)
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".