Detecting the Unstable Points in Deformation Monitoring Geodetic Networks in Analysis Method of Subnetwork
Bibliographic record
Abstract
One of the most crucial issues in engineering of structure and investigating ground deformation is deformation monitoring. The only thing which is strongly required is to create microgeodesy networks. An essential issue in microgeodesy networks is detecting unstable points of network. L1-Norm minimization and the global congruency can be noted as one of the classical methods for identifying network unstable points. In all previously conducted studies regarding this issue, results distinctly demonstrates that when displacement point vector is small, the number of points which have really displaced is more than that of true detection of displaced points using common deformation analysis ways. The probable reason for that can refer to spreading nature of the least squares estimation. Considering the results of recent studies in the detecting the network unstable points, to tackle the limitation the idea of subnetwork analysis is offered. In this case, some subnetworks including a subject point and the other source points appeared from dividing the deformation monitoring network. According to the unstable points, subnetworks will be there. This method will enable us to investigate the stable and unstable points. Having divided whole network to subnetworks, each network would be adjusted and unstable points of it would be detected. So, unstable points and their relations are cutoff and spreading effect of the least squares is fallen. This paper is on effort to evaluate the method in a simulated and a real network. The results prove that in a better and correct detection of unstable point can be successfully achieved by using subnetwork analysis compared to global congruency test all stimulates states proved the 35% of improvement on average. One percent of improvement in the results of subnetwork method to L1-Norm minimization cannot be acceptable. The algorithms of detecting unstable points in common methods and the method of analyzing subnetwork were conducted on a real network and the results are in line with simulated network results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".