COMPARISON OF CLEANING PERFORMANCE FOR ROW CLEANERS ON A STRIP TILLAGE IMPLEMENT
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
Strip-tillage implements remove the residue from previous crops and form a seedbed ready for planting.An experiment was conducted to evaluate 5 row-cleaning devices.The proportion of residue removed by the implement was used as the performance indicator.Each of the 5 devices was evaluated at 2 speeds and orientations on the implement.The devices were tested in two blocks (fields) of corn residue (one high residue and one medium residue), and one field of wheat residue.An analysis was conducted, using a mixed-effects model, to compare the performance of the cleaners operating in the different conditions.All cleaners performed well, with no statistical difference in mean performance.All row cleaners performed more consistently in wheat residue, compared with performance in corn residue.Numerically, the consistency of the different cleaners was different, with one configuration performing less consistently than the other four.Edge-effects of the outside row unit of the implement had, in most cases, an insignificant effect on the row unit's cleaning performance.'Electrical Power' labs as a teaching assistant has given me a new and different perspective on the learning experience.I know time is something we often find ourselves short of and I want you to know how much I appreciate the time you have all (Drs.Crowe, Roberge, and Noble) devoted to me throughout this project -from meetings and discussions to document reviewing.Jacky Payne played a critical role in the execution of this research.Without you, I would have been unable to complete the field trials.Thank-you for organizing and constructing the v prototype equipment for the tests, for your time and patience throughout the period we were in Texas, for sharing your extensive agronomic knowledge and expertise with me, and introducing me to authentic Texas-style BBQ...I would like to thank Tracey Meiners and Tim Olson from CNH Goodfield for providing me the opportunity to perform research on strip-tillage equipment produced at the Goodfield facility and for
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it