Relocating Large Trees for Germplasm Conservation in Tree Fruit Breeding Programs
Bibliographic record
Abstract
Mature seedling trees of pear ( Pyrus communis and interspecific hybrids), and fruiting trees of peach and nectarine ( Prunus persica ), apricot ( Prunus armeniaca ), and pear were relocated during the dormant season using tree spades. During the growing season immediately following, some signs of drought stress were noticed but all trees grew well enough that they could be used as a source of budwood for limited propagation purposes. When drip irrigation was supplied, supplemented by overhead irrigation as required, normal growth and production resumed within two growing seasons of the move. Some tree losses (less than 10% of trees moved) were reported from one site where the soil type was Fox sand with very poor water holding capacity. These tree losses were attributed to an inadequate water supply to the root ball, even though the site was irrigated. Our experience has demonstrated the feasibility of relocating relatively large trees, which can be beneficial for germplasm conservation in a tree fruit breeding program. However, it is probably not economically viable to relocate such trees for commercial production.
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 | 0.000 | 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 | 0.000 |
| 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".