Enhancing accuracy and precision of transparent synthetic soil modelling
Bibliographic record
Abstract
Over recent years non-intrusive modelling techniques have been developed to investigate soil–structure interaction problems of increasingly complex geometry. This paper concerns the development of a small-scale, 1 g, modelling technique using a transparent analogue for soil with particle image velocimetry for internal displacement measurement. Larger model geometry achieved in this research using fine-grained transparent synthetic soils has led to an increased need for rigorous photogrammetric correction techniques. A correction framework, based upon a modified version of the pinhole camera model, is presented that corrects for lens and camera movement induced errors as well as scaling from image space to object space. An additional statistical approach is also developed to enhance the system precision, by minimising the impact of increased non-coplanarity between the photogrammetry control plane and the target plane. The enhanced data correction and statistical precision is demonstrated using a case study examining the failure mechanism around a double helical screw pile installed in transparent synthetic soil representative of a soft clay.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".