SIMULTANEOUS ASSESSMENT OF COTTON YIELD MONITOR
Bibliographic record
Abstract
The most essential component of precision farming is the yield monitor a sensor or group of sensors installedon harvesting equipment that dynamically measures spatial yield variability. Yield maps, which are produced with data fromyield monitors, are extremely useful in providing a visual image to clearly show the variability of yield across a field. Inresponse to the demand for a reliable and accurate cotton yield monitor, several have recently become commerciallyavailable. We assessed the AgLeader, AgriPlan, FarmScan, and MicroTrak cotton yield monitors in southern Georgia forfive harvest seasons from 1997 to 2001. During 2001 we also assessed a prototype yield monitor. Each year, two or more yieldmonitors were mounted on a cotton harvester and were used during the harvest of several farmerowned and managed fields.The accuracy of each yield monitor was tested by comparing the weight of each harvested load to data produced by the yieldmonitor. Yield maps from each yield monitor were also produced with the respective software packages and compared.Features of the monitors were also compared. Each of the cotton yield monitoring systems we assessed has something to offera user interested in creating yield maps. All are capable of producing an adequate yield map provided the system is properlycalibrated, operated, and maintained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".