Genetic parameters for fruit mineral content in an interspecific pear ( <i>Pyrus</i> spp.) population
Bibliographic record
Abstract
ABSTRACT There is a lack of data and information on mineral content in pear fruit and none on the genetic parameters related to these individual elements. The aim of this research was to report mineral composition of pear fruit and to determine the quantitative genetic parameters of mineral content in fruit from interspecific pear populations. There was a high genetic component involved in the accumulation of boron and aluminium in the fruit, with heritabilities of as much as 0.72. Iron had a positive genetic correlation with accumulation of zinc (0.72) and sulphur (0.71), while chromium had a negative correlation with nickel (−0.77), phosphorus (−0.70) and magnesium (−0.4) accumulation. Parents of the same origin (Asian or European) had similar estimated breeding values for total mineral content. Macro element mineral content of fruit was: 4% calcium, 13% phosphorus, 76% potassium, 5% magnesium and 2% sulphur and for micro element mineral content 39% boron, 12% iron, 8% zinc, 4% manganese, 7% copper, 0.4% chromium, 9% nickel and 21% aluminium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".