Unmanned aerial survey in the summer season of the 67th Russian antarctic expedition
Bibliographic record
Abstract
The use of unmanned aerial systems (UAS) in glaciology and cryology, as well as studying and monitoring of polar regions is one of the most rapidly developing areas of the unmanned aerial industry. An aerial photogeodetic team of the 67th Russian Antarctic Expedition (RAE) solved two main interrelated tasks: 1) field tests of the newest Russian UAS Geoscan 701 in Antarctic conditions and 2) carrying out unmanned aerial surveys of two Antarctic territories, characterized by fundamentally different natural conditions, in order to obtain their high-precision orthomosaics and digital elevation models (DEMs) of an ultra-high resolution. On 15 January 2022, we carried out an unmanned aerial survey of two adjacent Antarctic maritime oases Molodezhny and Vecherny and surrounding areas of the glacier (Enderby Land, East Antarctica). From 26 January to 16 February 2022, we performed an unmanned aerial survey of the Fildes Peninsula (the southwestern, free of ice cover portion of the King George Island, South Shetland Islands, West Antarctica). The survey was complicated by severe meteorological conditions (low clouds, fog, strong winds, and precipitation). Field tests of UAS Geoscan 701 have shown that the system can be successfully used for unmanned aerial survey in polar regions. After in-office photogrammetric processing of the obtained materials, orthomosaics and DEMs of the indicated territories will be obtained with a resolution of 10 and 25 cm, respectively. These will be used for creation of modern large-scale topographic maps, photographic maps, three-dimensional and geomorphometric modeling of these territories, as well as operational and scientific activities of the RAE.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".