Obtaining and Evaluating New Hybrids of Papaya Tree
Bibliographic record
Abstract
Due to the close genetic base in the papaya crop, the breeding programs seek new alternatives with agronomic characteristics desirable to the producer and fruit that meets the consumer desire. The objective of this work was to evaluate the behavior of new Hybrids in germplasm database maintenance units of the company Caliman Agrícola SA. The experiment was carried out in a randomized block design with 10 new elements (CP3 × SSAM; CP3 × UENF/Caliman 01; CP3 × JS 12; CP2 × SS32; JS 12 × SSAM) and one control, UENF/Caliman 01, four replicates and ten plants per plot. Tem hermaphrodite plants per plot were evaluated at eight and 12 months after planting, 16 characteristics focused on plant morphologies and biometry of fruits harvested at the maturation stage II (fruits with up to 25% of the yellow surface). The productivity of one year of harvest was also evaluated. Among the new hybrids evaluated, it is possible to detect the presence of productive characteristics and fruit quality that were interesting for the market, suggesting that they be evaluated for crop value and use for future launches as commercial hybrids. With interest for future market launch, we highlight CP3 × 72/12, CP2 × SS32, CP3 × Progeny Tainung and CP1 × Sekati which shows high average productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".