Evaluation of Automated, In-Cockpit Swath Displacement
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
Highlights Automated swath displacement works well in high drift conditions. System relevance and performance are limited in low drift conditions. Testing against pilot skill without an automated system is necessary. ABSTRACT. A set of spray trials was designed to evaluate the onboard swath displacement feature that is offered as part of some avionic systems and used to aid in targeting aerial spray applications. The trials were flown in Miramichi, New Brunswick, Canada, and were intended to provide basic information on the capabilities of these systems. A total of 32 trials were run, of which 24 provided coverage data that could be used in this evaluation. Two aircraft types were tested, each with distinct avionics systems and automated swath displacement capabilities. Each aircraft was flown with two application setups, resulting in four application scenarios. The systems were generally able to allow the pilot to apply the spray with a swath peak within 5 m of the target line on average, even in high wind conditions. Other relationships that were anticipated in the data, such as a direct correlation between offset distance and targeting accuracy, were not observed. The systems do not appear to add much capability in low drift conditions when not much displacement is expected. It is recognized that a control data set is necessary to evaluate the extent to which the systems improve targeting accuracy. Keywords: Aerial management system, Aerial pesticide application, Drift offset, Precision application, Real-time modeling, Spray deposition, Spray drift, Swath displacement.
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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 | 0.000 | 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