Multiple Attributed Parametric Review Study on Mechanical Cotton (Gossypium hirsutum L.) Harvesters
Bibliographic record
Abstract
Crop characteristics of cotton are crucial to identify the important crop attributes like plant height, canopy width, sympods and monopods distribution, row spacing which affects the performance of mechanical harvesters. The activity and effectiveness of most harvest aids, including desiccants is reduced by low temperature conditions. Trash content was observed to be lesser in cotton harvested by cotton picker than cotton harvested by cotton stripper. It was found that a maximum cotton yield of 1000 kg acre-1 was obtained for a cotton plant population ranging between 45,000 and 90,000 plants acre-1. Likewise, a minimum of 700 to 740 kg acre-1 was observed for a cotton plant population of 33,000 plants acre-1. In higher yielding cotton, cotton pickers recorded higher picking rate than cotton strippers. Picking/harvesting efficiency of cotton stripper with both finger and brush type mechanism was higher than the spindle type cotton picker. Picking efficiency of pneumatic picker was higher than the other types of picking mechanisms, but with lesser rate of picking capacity. Gin turnout of cotton was higher with cotton picker when compared with cotton stripper due to lesser trash content in picker harvested cotton. The horsepower requirement of cotton stripper ranged from ½ to ¼ horsepower and cost is about two-thirds of the price as compared with cotton picker. The scheduling and monitoring of various activities involved in cotton picking by using a suitable software model can increase the benefits of both growers and harvesting companies. The reduction in uniformity with roller gin-type lint cleaners ranged between 0.2 to 0.8%, which was lesser as compared with saw-type lint cleaners. Introducing mechanical harvesting has always been a decades-long process. In Turkey, it took 20 years and in Greece, this process took place very gradually over a 15-year period. Top cotton producing countries like India, Pakistan, China, Uzbekistan and other developing countries like Iran Paraguay are still not using machine harvesting. The introduction of mechanical cotton picker or stripper can help improve quality and quantity of cotton picking thereby giving more benefit to growers in developing countries and improving their socio-economic status. The most controversial issue raised by the introduction of the mechanical cotton harvester is great migration as the machines eliminated jobs and forced poor families to leave their homes and farms in search for urban jobs. Therefore Government policies towards cotton harvesting mechanization must include the alternative jobs, packages for dependent manual cotton pickers and their families.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".