Preliminary identification and evaluation of parameters affecting the capacity of the operator-earthmoving machine system
Bibliographic record
Abstract
Without reliable data on the time of work of construction machines it is impossible to calculate the cost of the investment or the time limit for its implementation. Machine capacity is affected by many factors resulting from both the technical capabilities of a machine (e.g. the engine and bucket capacity) and work environment (e.g. soil loosening and weather conditions). Capacity is also influenced by factors affecting the operator (e.g. health condition, stress, fatigue). Therefore, it is appropriate to use the concept of the operator-machine system. The current system for the standards of machine working time collected in catalogues of capital expenditures is outdated (a lack of modern materials, technology and equipment currently used). It does not take into account all possible weather conditions, labour conditions and soil and water conditions. The result of this state of affairs may be overestimation or underestimation of an investment. On the basis of the conducted research it may be concluded that the greatest impact on the capacity of the operator-earthmoving machine system is exerted by parameters associated with the psychophysical condition of the operator (experience, fatigue, health and motivation of the operator) and the technical parameters of the machine (technical condition and theoretical technical capacity). Weather conditions, particularly air humidity, affect the performance to the smallest extent.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".