Comparing measured with simulated vertical soil stress under vehicle load: Are the measurements or the models wrong?
Bibliographic record
Abstract
The load transfer within agricultural soil is typically modelled on the basis of the theory of stress transmission in elastic media, usually in the semi-empirical form that includes the “concentration factor” (v). Measurements of stress in soil are needed to evaluate model calculations, but may be biased because transducers do not read true stresses. The aim of this paper was to measure and simulate soil stress under defined loads. First, we investigated the accuracy of the transducers in situ by measuring stress at high spatial and temporal resolution at 0.1 m depth under a known load. Stress in the soil profile at 0.3, 0.5 and 0.7 m depth was measured during wheeling at field capacity on five soils (13-66% clay). Stress propagation was then simulated with the semi-analytical model, using vertical stress at 0.1 m depth estimated from tyre characteristics as upper boundary condition, and v was obtained at minimum deviation between measurements and simulations. The transducer readings over-predicted the true vertical stress by 10%. Consequently, the measured stresses were corrected before further analysis. For the five soils, we obtained an average v of 3.9 (for stress propagating from 0.1 to 0.7 m depth). This was not significantly different from v = 3, i.e. v for homogenous, isotropic and linear-elastic material. We noted that v was strongly dependent on the accuracy of stress measurements, and on the upper stress boundary condition used for simulations. Finite element simulations indicate that for an elasto-plastic layered soil (topsoil over plough pan over subsoil) propagation of vertical stresses is not appreciably different from that in a homogeneous isotropic and linear-elastic soil unless layers with (unrealistically) high soil stiffness are considered. Our results highlight the importance of accurate stress readings and realistic upper model boundary conditions, and suggest that actual stress propagation was in line with predictions according to elastic theory for the conditions investigated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".