SEAMLESS PRECISE KINEMATIC POSITIONING IN THE HIGH-LATITUDE ENVIRONMENTS: CASE STUDY IN THE ANTARCTIC REGION
Bibliographic record
Abstract
Scientific activities in the Antarctic regions have increased daily within the last decades to achieve many different projects. The ice sheet over 98% of the Antarctic continent, the coldest, driest, and windiest place in the world and has the largest desert, makes it very difficult to conduct any kind of study and research. Among them, precise hydrographic surveying should be conducted for many different applications that require reliable and accurate positioning. The output from these surveys plays a vital role in understanding sea level changes, global warming, sea ice movement, navigation and many others. The harsh atmospheric and topographic conditions of the region pose additional challenges to surveyors in the use of conventional terrestrial measurement techniques and satellite-based positioning methods (GNSS) to make positioning. Low quality and noisy GNSS observations with low satellite elevations made their positioning vulnerable to cycle slip, multipath, and discontinuity in Antarctica. This study analyses the performance of the post-processed kinematic Precise Point Positioning (PPP) based on the web-based online GNSS processing service for marine surveying in the high-latitude environment. Within this frame, two realistic experiments were carried out on a ship and zodiac boat during the 6th Turkish Antarctic Expedition (TAE). The results show that the PPP coordinates using an online GNSS processing service provide kinematic positioning with centimetre level of accuracy using a single GNSS receiver. The general results showed that the PPP technique allows for much faster and accurate positioning in remote and high-latitude areas at a lower cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".