Fasulyenin Hasat-Harman Mekanizasyonunda Tane Kayıpları
Bibliographic record
Abstract
In this research it was aimed to improve different methods apart from manual harvesting requires intensive labour in drybean harvesting. For this purpose three different methods were used for bean harvesting-threshing and these methods weretested in fields where two different local bean population (Canada-Sarikiz) were cultivated.I. Method: Hand pulling, piling, threshing by thresher.II. Method: Cutting by double knife mower, piling, threshing by thresher.III. Method: Harvesting-threshing by prototype harvesting-threshing machine.Work efficiency, total grain losses and some characteristics related to plant mechanization were determined in thesemethods.Consequently, it was found that total grain losses in Canada and Sarikiz bean population were 9.029-6.955 % in the firstmethod, 25.279-22.301 % in the second method, 19.380-18.006 % in the third method, 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".