Genetic diversity and mating system of post-fire and post-harvest black spruce: an investigation using codominant sequence-tagged-site (STS) markers
Bibliographic record
Abstract
cDNA-based sequence-tagged-site (STS) markers were used to examine the genetic composition of three mature, layer-origin populations of black spruce (Picea mariana (Mill.) BSP), which were the result of logging operations in the first half of the 20th century, and compare them with four mature, seedling-origin populations that regenerated naturally following fire. The amount of STS-marker variation revealed in these populations was very similar to that previously observed in a rangewide panel of black spruce trees. There was little differentiation among populations, and no significant differences in heterozygosities, numbers of alleles, or fixation indices were evident between layer-origin and fire-origin stands. Likewise, when mating-system parameters were estimated in one population of each of these two types, no significant differences were found; outcrossing was essentially complete with no evidence of mating among relatives. The estimated correlation of paternity within progeny arrays was about 17 and 13% in the fire-origin and layer-origin stands, respectively, but again the observed difference was not statistically significant. At least at the current scale of sampling, silvicultural practices that result in stand replacement by layer-origin advance regeneration appear not to have had negative impact upon the genetic diversity or level of inbreeding in second-growth black spruce stands.
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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".