Analiza deformacij s helmertovo transformacijo : Deformation analysis with the helmert transformation
Bibliographic record
Abstract
In the second half of the previous century, analysis of deformations became a highly interesting subject of research and practical application in industry. The presented approach was developed in British Columbia, Canada, for the determination of potential deformations on several dams and landslide areas. Original measurements were obtained via standard surveying observation of distances and directions for horizontal positioning, and elevation differences for vertical positioning. GPS vectors were added some years later. The geodetic measurements of several epochs were adjusted with the parametric model of the method of least squares with minimum datum constraints. The deformation analysis was made with the successive application of the Helmert transformation ; V drugi polovici prejšnjega stoletja je analiza deformacij postala zelo zanimiva tema raziskovanja in praktične uporabe v industriji. Predstavljeni pristop obravnave deformacij je bil razvit v kanadski provinci Britanska Kolumbija. Z njim smo želeli določiti potencialne deformacije na nekaj vodnih pregradah in plazovitih območjih na tem območju. Prvotna opazovanja so bila standardna geodetska opazovanja dolžin in smeri za horizontalni položaj in višinske razlike za višinsko predstavitev. Nekaj let pozneje so bili dodani vektorji GPS. Geodetska opazovanj v različnih epohah so bila izravnana s posredno izravnavo po metodi najmanjših kvadratov na podlagi minimalnega števila vezi med neznankami. Deformacijska analiza je bila izvedena iterativno z uporabo Helmertove transformacije.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".