Deformation analysis: the modified GREDOD method
Bibliographic record
Abstract
https://www.geodetski-vestnik.com/en/clanek/10.15292/geodetski-vestnik.2022.01.60-75 In this paper, a modified Generalised Robust Estimation of Deformation from Observation Differences (GREDOD) method is presented, based on the application of genetic algorithm (GA) and generalised particle swarm optimisation (GPSO) algorithm in solving the optimisation problem of this method, which is, in essence, a problem of determining the optimal datum of the displacement vector. The procedure of deformation analysis using this modification of the GREDOD method is demonstrated in the example of the two-dimensional geodetic network presented in numerous research and in which all observations and displacements were simulated. Using both algorithms, GA and GPSO, almost identical results of deformation analysis were obtained, except datum solutions of the displacement vector, which are completely different. These results differ only slightly from the results obtained using the methods of Hannover, Karlsruhe, Delft, Fredericton, München, Caspary, and the classical robust method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".