Deformacijska analiza po postopku fredericton ; Deformation analysis: the fredericton approach
Bibliographic record
Abstract
V članku je opisan postopek Fredericton, ki je eden izmed postopkov deformacijske analize. Za opis razlik med geodetskimi opazovanji ali koordinatami točk v geodetski mreži v več terminskih izmerah predpostavimo več mogočih deformacijskih modelov. Na podlagi statističnih testov in razpoložljivih informacij o dogajanju na obravnavanem območju izberemo najboljši deformacijski model. V članku je najprej podano teoretično ozadje postopka, nato je postopek uporabljen na primeru generiranih opazovanj v dveh terminskih izmerah. Rezultati postopka Fredericton na obravnavanem primeru niso bistveno odstopali od rezultatov postopkov Delft, Karlsruhe in Hannover ; In this article, the Fredericton approach to deformation analysis is presented. It is possible to use several deformation models to determine the differences between the geodetic observations or between the coordinates of points in geodetic network in more epochs. The most appropriate deformation model has been chosen based on statistical testing and available information about dynamics at the area of interest. First, a theoretical background of the approach is described. Then it is applied to the generated observations in two epochs. In the present example, the results of the Fredericton approach differ only slightly from the results obtained with the Delft, Karlsruhe in Hannover approaches.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".