Bumpiness problem and its remedy in Papaya (Carica Papaya)
Bibliographic record
Abstract
Papaya (pawpaw) Carica papaya L. belongs to family Caricaceae. Papaya is a very good source of fruit sugar, vitamin A, B and C. This fruit is rich in minerals and salts and makes very good food. Fiji's climate is very suitable to grow papaya and Fijian grown papaya has a big export market. Main importing countries so far are New Zealand, Japan and Canada. Another potential country for exporting papaya from Fiji is Australia. However, strict quality control and high sanitary requirements must be met to export papaya to Australia. Papaya export has gone up in last few years but unfortunately there has been no export so far to Australia. Fruit's shape, size and smoothness are important determinant factors for export market. Misshapen fruits with bumps are not acceptable in overseas market. Similarly most importing countries prefer medium sized fruits. To get good quality papaya particularly fruits without bumps, it is necessary to apply Boron in soil. Results obtained in the present investigation showed that 5.0kg Boron (applied as borax pentahydrate) per hectare was very effective in reducing bumpiness to a very minimum thus improving the quality of fruits. Boron as such showed no effect on papaya yield per plant. Three cultivars tested for average fruit weight showed acceptable fruit weight for local and export market. However, Solo Sunrise was identified as the highest average fruit yielding cultivar (tons/hectare). Improvement in quality of papaya will open up new markets for export.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".