Graft survival and promotion of female and male strobili by topgrafting in a third-cycle slash pine (<i>Pinus elliottii</i>var.<i>elliottii</i>) breeding program
Bibliographic record
Abstract
A total of 2561 slash pine ( Pinus elliottii Engelm. var. elliottii) grafts were topgrafted in the winter of 2003 and evaluated in January of 2004. The objectives of this study were to understand the effect of the genetic material and crown position on survival and flowering response of topgrafts. Also, the effects of geographic direction, branch order, and scion age on topgraft response were assessed. Topgrafting was an effective tool for promoting both female and male strobili. The genetic material (scion and interstock clones) and the crown position had large effects on the promotion of female flowering and topgraft survival. More than 23% of the total variation in female flowering and 16.3% of the total variation in topgraft survival were due to differences among scion clones and among interstock clones, respectively. The highest survival rate was reached by grafting in the mid-top followed by the top crown position. Grafting in the top of the crown was highly superior in promoting female strobili followed by the mid-top position. First-order branches showed a significantly superior production of female strobili. Chronologically older scions, from selections made in the first and second generations of the tree improvement program, produced more female and male strobili than third-cycle forward selections.
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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 | 0.000 | 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) | 0.000 | 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".