Bibliographic record
Abstract
The objective of this study was to test the performance of a Field-IQ sprayer control system which was interfaced with a John Deere liquid fertilizer applicator. The sprayer control system includes Case IH FM-1000 display integrated with DGPS, Field-IQ spray control with full harness, flowmeter and auto-range valve, and the pump. Tap water was used to simulate the UAN 32% nitrogen fertilizer solution in the test. A total of 120 samples from 8 rows of the applicator in 5 application rates (34, 67, 101, 135, and 168 kg/ha) were collected, and the data were analyzed for application uniformity and application rate accuracy of the system. Results showed the greatest variation from the application rate baseline among the rows was 10.1% and occurred at a swath-end row with the lowest application rate of 34 kg/ha. The rows in the middle section of the swath had the lower variation than the rows at the two swath-ends. The application uniformity increased as the application rate increased. The absolute error of application rate ranged from 1.3% to 6.5% with an average of 3.76%. There was no significant difference between the selected application rate and actual application rate (p > 0.8686).
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 | 0.003 |
| 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) | 0.001 | 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 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".