Using VR for Efficient Training of Forestry Machine Operators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the results of two years of field trials of a 3D graphical simulator of forestry machines called processing harvesters, for the training of students in wood harvesting. It is a comparative study of the results between the traditional training where students go directly from the classroom to real machine operation in the woods in a new VR augmented training. The results indicate that the addition of 25 hours of hands-on VR training increases by 23% the volume of wood harvested and reduces by 26% the repair and maintenance costs during the first month of operation in forest. The use of VR also allowed precise recording and monitoring of the evolution of trainees' performance during their training sessions, showing learning curves that decrease with time for all the defined performance criteria (execution time, error rate and precision). The field trials were held in a training center with four classes of eleven students in wood harvesting and are the first know experiments concerning the use of virtual reality technologies for the training of students in forestry.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it