Germination and initial growth of half-sibling families of Pinus pseudostrobus Lind. outstanding in resin production
Bibliographic record
Abstract
The establishment of commercial forest plantations necessitates the use of high-quality genetic seed and the selection of superior individuals. This study aimed to evaluate the germination ability including germination percentage, germination velocity index, and germination rate as well as the initial growth characteristics (height and root collar diameter) of seedlings from 23 half-sibling families of Pinus pseudostrobus Lindl., selected for their exceptional resin production, across three altitudinal gradients. Half-sibling seeds of P. pseudostrobus were sown in 310 cm3 plastic-coated containers filled with a 1:1:1:1 substrate mixture of pine bark, Canadian sphagnum peat moss, vermiculite, and perlite, supplemented with Multicote® fertiliser at a concentration of 5 kg/m3. Each family was represented by four replicates, or experimental units, consisting of 25 completely randomised seeds. The evaluated variables were as follows: 1) germination percentage (PG), 2) germination vigour (VG) through the index of germination velocity (IVG), 3) germination rate (TG), 4) plant height (A), and 5) diameter of the root collar (DCR). Significant differences (p < 0.05) were found in the germination percentage and index of germination velocity between altitudinal gradients and between families in all measured variables. Our results suggest that germplasm collection should be performed within a gradient between 2,200 and 2,400 m, where seeds present the most intraspecific variation and the highest germination percentage, height, and root collar diameter.
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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) | 0.001 | 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".