Mer Bleue, Ontario, Arctic surrogate study site project, 2016: global navigation satellite system survey report
Bibliographic record
Abstract
Natural Resources Canada (NRCan) has the mandate of providing essential geographic information. An improved knowledge of our physical environment represents one of the cores of this mandate. The Arctic is an important but challenging region to study, especially for wetland monitoring. To reduce survey costs, researchers often use surrogate sites located in less remote areas. The Mer Bleue Bog Peatlands, a conveniently accessible sub-arctic wetland similar to wetlands found in the Arctic environment, is being used as arctic surrogate study site for the MBASSS Project. This study site is used for the calibration and validation of various types of optical (spectral) remote sensing data acquired by several project partners using satellite, airborne and Unmanned Aerial Vehicle (UAV) platforms. Precisely geo-located products require ground control points (reference points) which are visible to the sensor on the platform and whose geographic location is known with precision. To fulfill this need, high precision GNSS surveys are required. This highly illustrated document describes in detail the methods and results of the GNSS surveys required for the geographic rectification of imagery, including Unmanned Aerial Vehicle photographs, airborne hyperspectral imagery, and space borne multi-spectral imagery acquired within the scope of MBASSS during 2016.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.008 | 0.005 |
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".