Bibliographic record
Abstract
This article reviews some ergonomic factors associated with agricultural guidance displays. Any technology or management decision that improves the efficiency of an agricultural operation can be considered an aspect of precision farming. Agricultural guidance displays are one such tool because they help to reduce guidance error (i.e., skipping and overlapping of implements within the field), which result in improper application of crop inputs at increased cost. Although each of the guidance displays currently available functions using a different principle, their key objective is to communicate useful guidance information to the operator of the agricultural machine. The case with which the operator obtains the required information depends on a number of ergonomic factors, such as color perceptibility, flash rate, attentional demand, display size, viewing distance, and height of placement of the display in the cab. Ergonomics can be defined as the application of knowledge to create a safe, comfortable, and effective work environment. Consequently, it is critical to consider ergonomics when designing guidance displays or when locating a display in the tractor cab. Without considering ergonomics, it is unlikely that the efficiency of the human-machine system can be optimized.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".