An assessment of seasonal variations in the cref cors at the University of Lagos
Bibliographic record
Abstract
Continuously Operating Reference Stations (CORS) are reference stations of the Global Navigation Satellite System (GNSS), providing fundamental positioning infrastructure that is accurate and reliable. As such, CORS are designed to meet the needs of a wide range of users requiring high three-dimensional (3D) positional accuracy. The Continuously Operating Reference Station at the Engineering Faculty (CREF), University of Lagos was set up in order to support research applications in Surveying, Mapping and Geodesy. This study evaluates the seasonal variations in the 3D coordinates of CREF using metrics such as coordinate differences, Standard Deviation (SD) and Standard Error (SE). The Canadian Spatial Reference System (CSRS), known as CSRS – Precise Point Positioning (CSRS-PPP) was used to compute the station’s daily coordinates over a three-year period from 2016 to 2018. In the analysis, the daily coordinates were divided into two seasons - the wet and the dry. The results obtained show that the dry and the wet seasons had SDs (5.4 mm, 3.9 mm, and 2.0 mm) and (5.2 mm, 18.6 mm and 14.4 mm) in the x, y and z-directions respectively. Generally, the dry season presents a better result than the wet season as revealed by the accuracy metrics. These results have led to an increased understanding of the seasonal variability inherent in the data acquired by GNSS CORS, and must be taken into consideration: in particular, for GNSS applications such as the weather prediction and water vapour estimation. This study concludes that more needs to be done regarding the maintenance of CREF to ensure data continuity and reliability for geodetic studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".