Justification for Remote Control of Construction and Road-Making Machines
Bibliographic record
Abstract
It is evident from the experience of operating the construction machinery (excavators, bulldozers, loaders etc.) that quite often the machinery and its operators are working under severe conditions. Working under threat of rock fall, on unstable or contaminated grounds, or debris handling tends to increase the impact of adverse occupational environment on the operators’ health. When the operators are at risk, it is advisable to deploy remote process control technologies on the work sites. This is what brings research and development of modern remote control systems to the top of the chart in order to improve the productivity of machinery, enhance the safety and quality of the jobs carried out. The use of remote control will exclude the adverse impact of aggressive environment during the process operations. The research allowed to establish that the design specifics of the construction and road-making machines enable them to go far beyond the standard process layout giving into a much wider range of application, up to aggressive environments. A crawler excavator model was devised, imitating the visibility range of the process areas for the manual and remote control systems. Implementation of remote control for construction and road-making machines may become a step towards a completely new level of interaction within the man-machine-environment system.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".