Bibliographic record
Abstract
During the experimental years of 2017 and 2018 eight potato cultivars were evaluated for their growth traits and productivity in tuber yield. The potato cultivars used in these experimental seasons were; Bubu (as a standard check), Chiro, Gebisa, Belete, Gudene, Badasa Jarso and Dedafa. The first six consecutive potatoes are improved cultivars which were released by Ethiopian research institute and University while the last two potatoes were collected from farmers and cultivated as local (native) cultivars. The objective of this study was to compare the growth and productivity potato cultivars grown at eastern regions of Ethiopia. The results revealed that performance of improved potato cultivars were high in both evaluated yield related traits and average tuber yield. The performances of evaluated potatoes were not similar among cultivars and within cultivar throughout experimental seasons. The experimental design was a randomized block design in three replications. The field management; seed tuber selection, land preparation, planting, fertilizer application method and amount, ridging, weeding, cultivation, harvesting, data collection method and collected data analysis has been carried out for all potato cultivars in similar manner. Potato tubers from four middle rows were analyzed for parameters such as tuber yield, tuber number, marketable and total tuber yield. The highest tuber yield was revealed with cultivar Bubu (39.4 t ha-1) while the lowest with Jarso (20.89 t ha-1). The highest tuber number was showed with cultivar Badasa (12.73 plant-1) and the lowest with Belete (7 plant-1). Hence, there were no correlation between average tuber yield and number.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".