Correlation between seedling length and Canadian Douglas-fir height
Bibliographic record
Abstract
This paper presents the results of the studies of Douglas-fir seeds from Canada aimed at understanding and controlling the processes of the genetic growth potential of Douglas-fir in Serbia. The research was focused on the early stage of Douglas-fir growth, i.e. at the stage of seed germination in the laboratory. We tested the correlation between seed germination, seedling length and the height of plants in the nursery. The seeds from 13 Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) localities were obtained from Canada and compared under laboratory and nursery conditions. The seeds from different altitudes, latitudes and longitudes come from a part of the natural range of Douglas-fir in Canada. Before they are transferred and introduced, seed material must be tested with regard to the potential success of the selected tree species within the shortest possible time. This is necessary for the introduction of a tree species with a widespread natural range. Douglas-fir is a highly productive coniferous tree species with a broad geographical and ecological range. It has a wider natural range than other conifers and greater chances of successful adaptation to new ecosystems. Descriptive statistics, analysis of variance for regression, regression and correlation were used to analyse the data. A strong correlation was established between the height of four-year-old seedlings and seed characteristics (germination rate and seedling length).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".