Responses of potato (Solanum tuberosum L.) var. Agria to application of bio, bulk and nano-fertilizers
Bibliographic record
Abstract
Potato (Solanum tuberosum L.), is one of the important crops grown in the world which is important as food and nutritional security option at the global level. The experiment was laid out as randomized complete block design in three replications with six nutrition treatments consisted of control, NPK, Mog bio-fertilizer, Nano-Ca, Nano-Zn+B and Nano-Com. The treatment-by-trait (TT) biplot analysis was applied to data to examine its usefulness in visualizing relationships among trait as well as treatments and showed that the first two principal components accounted 80% of total variation. Tuber yield, mean tuber diameter, mean tuber weight, tuber weight per plant, starch content of initial fresh, number of tubers per plant, number of leaves and dry matter content were in the same sector, with Nano-Com fertilizer treatment as the best treatment. Based on ideal entry biplot, the Nano-Com treatment is closest to the position of an ideal treatment and it is ranked the highest in term of morphological performance. Also, the best fertilizer treatment for obtaining of high tuber yield could be found as Nano-Com treatment following Nano-Zn+B treatment. The studied nanofertilizers showed a good potential compared to the commercial bulk and bio fertilizers.
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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) | 0.001 | 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".