Bibliographic record
Abstract
An autonomous machine is a machine that can navigate through its environment without human interactions.These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement.At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed.This low level machine was the sensor system for an autonomous machine.The machine was capable of sensing the environment and carrying out actions based on commands sent to it.This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment.The tractor was equipped with sensors capable of sensing the speed and heading of the tractor.A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed.The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided.The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow.The sensor platform developed was found to be insufficient for proper control.The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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 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".