Análisis de los residuales en el calculo de velocidades geocentricas a partir de series de tiempo diarias PPP
Bibliographic record
Abstract
The online processing of GNSS observations has been increasing in recent years. Different services such as the one offered by the Canadian Space Reference System (CSRS), under the Precise Point Positioning (PPP) modality, offers the possibility of knowing in a fast and effective way the three-dimensional position from a rinex file. Taking advantage of these results, daily files were processed for a period of 2.5 years and for 10 GNSS stations in Costa Rica which are integrated into the Sistema de Referencia Geocéntrico para las Américas (SIRGAS). The geocentric coordinates derived from the on-line PPP processing were considered as observation vectors for the estimation of their velocities (Moya et al, 2017). However, a statistical test called the outliers test (Pelzer, 1985) was previously programmed and implemented for the estimation of potential atypical observations. This test is frequently used in the analysis of geodetic networks (Knight et al, 2010), however, it was now applied in the linear velocity calculation model. The adjustment process for the calculation of velocities was done iteratively per station and per coordinate, excluding the outlier observations marked by the test in each process. On average, this test detected a series of observations with residuals ranging from -50 mm to 40 mm in its three components. Finally, with the purified velocities of oitliers, they were validated with respect to the SIRGAS multiannual solution SIR15P01, taking as reference time 2017.0. The average differences between the two determinations were _15 mm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".