Ground control point acquisition for Acadia Forest, New Brunswick, during winter 2016, in support of Canada Centre for Mapping and Earth Observation snow depth from unmanned aerial vehicule activities.
Bibliographic record
Abstract
Natural Resources Canada has the mandate of providing essential geographic information. An improved knowledge of our physical environment represents one of the basis of this mandate. This knowledge is generally associated with the environment as it appears in summer. However, snow cover is present over most of Canada for a varying period of time during the year. Managers, engineers and researchers require up to date information with regard to snow: Its coverage, depth and water content. These elements are difficult to accurately measure and any initiative in this area must use a stepwise approach; mapping the snow surface extent, and monitoring of snow melt being the first steps of this process. Flood monitoring / forecasting practitioners also require up to date information about snow cover, specifically in the spring season. For the last few years, versatile and low cost Unmanned Aerial Vehicles (UAV's), also called drones, allow for multitemporal aerial surveys of snow cover to extract various representations of the snow extent. To obtain products geographically located with precision, it is required to establish ground control points (reference points) which are visible to the UAV camera, and for which geographic location is known with precision. To fulfill this need, a high precision Global Positioning System (GPS) survey is required. A case study was undertaken in a test site of a few tens of hectares located in the Acadia Forest near Fredericton, New Brunswick. This document describes in detail the method and results of the GPS survey required for the geographic rectification of the numerous photographs acquired by the UAV's within the scope of this project.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".