In vitro rooting of hybrid hazelnuts (<i>Corylus avellana</i> × <i>Corylus americana</i>) in a temporary immersion system
Bibliographic record
Abstract
Commercial micropropagation of hybrid hazelnuts (Corylus avellana L. × C. americana Marshall) has been limited, owing to their poor rooting ability in vitro as well as ex vitro, leading to high mortality of plantlets transplanted in the greenhouse. The objective of this study was to develop an efficient and cost-effective protocol for rooting and plantlet acclimation of in vitro grown hazelnut shoots. Efficient in vitro rooting was accomplished in a rocker-based temporary immersion bioreactor system. The use of a temporary immersion system (TIS) in combination with the inert substrate Oasis® In Vitro Express (IVE) significantly improved the in vitro rooting efficiency (100%) compared with semi-solid medium (27%) after four weeks of culture. A higher density (36 explants/vessel) of shoot explants in the TIS was found to support a significantly greater shoot height, chlorophyll content, and longest root length, compared with the lowest density treatment (12 explants/vessel). Efficiency of rooting and the number of roots formed were similar for both the high and low density of explants in the culture vessels, and the resulting plantlets exhibited > 80% survival in the greenhouse. These results demonstrate the usefulness of rocker-based TIS for commercial micropropagation of hazelnuts and, potentially, other tree species.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".