Growth and survival of Siberian larch in Alberta at the species, population, and family levels
Bibliographic record
Abstract
Survival and growth of Siberian larch (SL, Larix sibirica Ledeb.) were compared with those of three conifer species native to Alberta, Canada: lodgepole pine (LP; Pinus contorta var. latifolia Engelm. ex S. Watson), white spruce (WS, Picea glauca (Moench) Voss), and jack pine (JP, Pinus banksiana Lamb.) at 12, 10, and 3 trial locations, respectively. The average data age was 18 years (range: 3 to 27 years). Survival of SL averaged 4.2% and 6.5% worse than LP and WS, respectively, while it was 5% better than JP. SL grew 25%, 94%, and 23% taller than LP, WS, and JP, respectively. Stem forking rates were similar between SL and LP, WS, and JP. The best seed sources for Alberta were mature trees established in Alberta and Saskatchewan of unknown initial provenances. The Russian Altai Mountain source grew well at high elevations, while the Finnish Raivola performed well in the northern, low-elevation area. Open-pollinated progeny tests of 58 families planted in five diverse locations yielded individual-tree narrow-sense heritabilities and family mean heritabilities for height at age 15 of 0.15 and 0.59, respectively. The type B between-site genetic correlation was 0.44, indicating a strong genotype × environment interaction. SL has performed well in Alberta, and its growth can be further improved by selection and breeding from appropriate seed sources.
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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".