Determination of Displacement Geodetic Network Points, Fredericton Approach
Bibliographic record
Abstract
This graduate thesis deals with the Fredericton approach for determining displacements in geodetic networks. In the introduction strain analysis is presented from a geodetic point of view. Special emphasis is placed on the problem of geodetic datum. It is followed by a theoretical explanation of the method in five steps: adjustment of observation for each epoch, preliminary identification of deformation models, estimation of deformation parameters, checking the deformation models and selecting the best one, graphical presentation of the selected deformation model. The method was applied to observations made in a relative geodetic network Pesje in two epochs. The network did not have defects of configuration but a datum defect was present from the use of the coordinate approach. The results differ slightly from the results obtained from the Delft, Hannover and Karlsruhe approaches and even more from the results obtained from the Munich approach. Compared to other methods, the Fredericton method is less automatic since it requires a human decision on the preliminary identification of deformation models. The advantage of this method is its general applicability, which can be achieved by adapting the method to specific situations within a geodetic network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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) | 0.005 | 0.002 |
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".