NEW SPECIFICATIONS OF THE THEORY OF GROUND CUTTING
Bibliographic record
Abstract
Earthwork is performed by some digging machines: excavators, scrapers, graders, dragline excavators, ditching machines, boring installations, etc. Some researchers were engaged in the cutting of soil, and in general, the theory of cutting of soil is rather developed. However, its provisions are correct to the determination of machines of the soil performing almost rectilinear cutting close to a surface, in a dehydrated medium. As practice shows, the known provisions of the theory of cutting do not allow to calculate accuracy the mode and loading of these machines. This article is devoted to the establishment of the regularities determining soil resistance to destruction for construction boring and milling machines. The determined dependencesdetermining the law of change of a corner of a chip when cutting soil as a function from the coupling, pressure in soil, hydrostatic and hydrodynamic pressure upon a surface, the angle of cutting, the angles of internal and external friction are determined. Coefficients of working conditions which describe a high-quality picture of cutting of soil are received and give a ratio of forces in case of rectilinear destruction of soil to forces arising in case of its milling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".