SnowMicroPenetrometer applications for winter vehicle mobility
Bibliographic record
Abstract
The U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) provides cold regions research and development in support of the US military and the nation.For winter military operations this support includes vehicle mobility modeling over snow.Many factors relate to vehicle performance, fuel efficiency and operation efficiency, including the vehicle specifications and the land surface conditions.Comprehending snow macromechanical characteristics -such as elastic modulus, stiffness, and strength -is critical in understanding how effectively a vehicle will travel over snow covered terrain.Vehicle instrumentation data (inertial measurement units and vehicle telemetry) and observations of the snow pack (both satellite and ground-based) are leveraged to improve the modeled index for winter vehicle performance.Currently, the available mobility models are physically-based and consider numerous factors related to cross country mobility such as slope, soil type, terrain strength, land classification and vegetation.The algorithms related to the impact of snow, however, are driven by snow depth and bulk snow density alone.This research deployed a SnowMicroPenetrometer (SMP) whose capabilities were expanded to measure several types of snow, including virgin snow, vehicle tracked snow and processed or groomed snow roads.The SMP highresolution snow structural profiles show the value of the instrument as a tool for mobility studies.Correlation analysis was conducted between the SMP and Rammsonde penetrometer using median values from different snow types at a particular site.The data express a trend that rupture force, penetration force, density, strength, and ram hardness increases when the snow is deformed by the vehicles.Instrument modifications were assessed with recommendations made to further improve SMP performance for use in mobility studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".