Patterns of allozyme variation in tamarack (Larix laricina (Du Roi) K. Koch) from northern Ontario / by Chao-wei Liu. --
Bibliographic record
Abstract
Roots from approximately 30 trees from each of 44 populations across the range of tamarack (Larix laricina (Du Roi) K. Koch) in northern Ontario were analyzed electrophoretically for allozymic variation in 14 enzyme \nsystems coded by 20 loci. A low level of variability was found in this conifer. On average, 22.7 - 28.8% of the loci per population were polymorphic depending on the criterion of polymorphism, with a mean of 2.60 alleles per polymorphic locus. Expected and observed heterozygosity per population were 0.091 and 0.087, respectively. G-tests for allelic homogeneity among populations indicated genetic heterogeneity (p < 0.05) at four loci. Approximately 4% of the total genetic diversity resided among populations. The mean genetic distance over all pairs of tamarack was 0.0045. Genetic distance was significantly related to geographic distance, and the latter accounted for 9% of the variation \nin genetic distance. Seven significant (p < 0.05) canonical discriminant functions accounted for 67.4% of the total variation at the polymorphic loci. Genetic variation in tamarack appeared to be affected by the environmental variables. An evolutionary bottleneck might be responsible \nfor the low variability. The relatively short colonization since glaciation seemed the most likely factors causing the relatively low differentiation among populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".