Comparisons of genetic diversity in white spruce (<i>Picea glauca</i>) and jack pine (<i>Pinus banksiana</i>) seed orchards with natural populations
Bibliographic record
Abstract
Genetic diversity within a white spruce (Picea glauca (Moench) Voss) seed orchard (consisting of 40 clones) and a jack pine (Pinus banksiana Lamb.) seed orchard (31 clones) was assessed and compared with genetic diversity in natural populations within the source area for the orchards. Genetic diversity was determined at 18 allozyme loci for seven white spruce populations and 27 loci for five jack pine populations, and the two orchards. Gene diversity maintained within the seed orchards (He = 0.157 for white spruce and 0.114 for jack pine) was similar to that found within the source area for each species (He = 0.164 and 0.114 for white spruce and jack pine, respectively). However, nine white spruce alleles and 12 jack pine alleles identified in the source area were not present in the seed orchards. These alleles occurred at low frequencies in the natural populations (mean frequency = 0.023 and 0.014 for white spruce and jack pine, respectively). Mean genetic identities between the seed orchards and their natural populations were high (>0.99), indicating that common allele occurrences and frequencies were similar between the orchards and their source area. One allele in the white spruce orchard and two in the jack pine seed orchard did not occur within the natural population samples. Simulations indicated that randomly reducing the number of clones within the seed orchards would decrease allelic richness slightly but would have little effect on overall gene diversity.
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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 | 0.000 |
| 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".