Technical, Field, Economic and Energy Comparison of Cutter Bar Maize Header With Snap Roll Maize Header
Bibliographic record
Abstract
In India, most of the maize combine harvester currently being used employs snap roll type header. This type of header is costly, dependent on row spacing of maize crop and causes losses at headlands during turning. Moreover owing to its heavy weight its frequent lifting and downing during harvesting season causes hydraulic leakages in certain sections of combine. Therefore to overcome these problems a new light weight cutter bar Maize header is developed and evaluated for maize crop. The performance evaluation of the cutter bar type maize header is done in a dislodged and a partially lodged (30-40%) maize crop. For lodged crops, the header losses varied from 19.18-26.71% and for dislodged crops it was varied from 5.29-10.15% respectively. The cylinder losses for dislodged crop varied from 2.70-2.86% and for lodged crop it varied from 0.85-2.04%. The mean cleaning efficiency for lodged and dislodged maize crop was found as 88.87% and 90.58% respectively. The grain damage for lodged and dislodged crop was observed as 8.31% and 5.94% respectively. The trash content for lodged and dislodged crop was 2.75 and 3.45% respectively. The performance of snap roll and cutter bar was also done. Total losses with snap roll header were higher as 15.06% and lower for cutter bar as 10.85%. The brokens were higher for cutter bar as 5.94 and lower for snap roll as 3.45%. The trash content was 3.45% for cutter bar header and 2.24% for snap roll header. The total energy input in snap roll header, cutter bar maize header and maize dehusker cum sheller were 2360.05, 1970.90 and 3770.48 MJ/ha respectively.The cost of operatin with cutter bar maize header, snap roll maize header and maize dehusker cum sheller were 53.62 $/ha, 68.73$/ha 187.32 $/ha respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".