SESSION XI: ACTIVITY OF THE FIG WORKING GROUP 6.1 ON DEFORMATION MEASUEREMENTS AND CLOSING REMARKS 390 The 10th FIG International Symposium on Deformation Measurements MODELS AND TERMINOLOGY FOR THE ANALYSIS OF GEODETIC MONITORING OBSERVATIONS- Official R
Bibliographic record
Abstract
The paper in hand is the result of the studies of the Ad-Hoc Committee ‘Classification of Defor-mation Models and Terminology ’ of Working Group 6.1 ‘Deformation Measurements’, FIG Commission 6. The Ad-Hoc Committee was set to work due to a recommendation of the chairman of FIG-Working Group 6.1 on the occasion of the 6th International FIG-Symposium on Deforma-tion Measurements in Hannover in 1992. Earlier reports were given on the 7th International FIG-Symposium on Deformation Measurements in Banff, Canada in 1993, on the Perelmuter Work-shop on Dynamic Deformation Models in Haifa, Israel in 1994, and in 1996 at the 8th International FIG-Symposium on Deformation Measurements in Hong Kong. A summary of the studies was presented at the 9th FIG-Symposium on Deformation Measurements in Olsztyn, Poland in 1999. The closing session of this symposium was devoted to a comprehensive discussion on the topic. It was concluded by the chairman of Working Group 6.1 summarizing the activities concerning the methods and models of deformation analysis during the past two decades. The paper here as sub-mitted to the 10th FIG International Symposium on Deformation Measurements in Orange, Cali-fornia in 2001 is the official final report of the Ad-Hoc Committee and includes the proposals
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.130 | 0.117 |
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".