Population variation in growth and 15-year-old shoot elongation along geographic and climatic gradients in black spruce in Alberta
Bibliographic record
Abstract
Twenty black spruce (Picea mariana (Mill.) BSP) populations from Alberta were tested at one trial to study the variation of shoot elongation at ages of 15 years, height growth at 10 and 15 years, and breast-height diameter (DBH) at 14 years. Significant difference among populations was found for all of the growth traits and some of the shoot elongation traits investigated. Population means for shoot elongation and cumulative growth traits of significant difference were further regressed against the geographic coordinates and climates of seed origins to study patterns of genetic variation in relation to geography and climate. Both linear and quadratic regressions were investigated, but the one with better fit (lower P and standard error) was chosen and further analyzed. Geographic and climatic gradients explained 20%62.9% of the population variation in regressions that were statistically significant. Shoot elongation and cumulative growth traits were closely related to frost-free periods, but diverged in their relationships to geographic and all other climatic variables considered. While shoot elongation was associated exclusively with latitude, longitude, day length, and negative temperature variables, growth traits were associated with elevation and positive temperature and moisture variables. Climate factors were more effective than geographic coordinates in describing differentiation in shoot elongation and growth traits. The most effective factors in predicting growth traits were mean annual precipitation and summer moisture index of the seed origin.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".