Spatial genetic structure within four tamarack (Larix laricina (Du Roi) K. Koch) populations in Northwestern Ontario / Heather A. Foster
Bibliographic record
Abstract
This study was conducted to test the null hypothesis \nthat tamarack (Larix laricina (Du Roi) K. Koch) populations \nare random assemblages of genotypes, and that this absence \nof pattern can be observed on sites with differing \necological and demographic characteristics. A total of \n1715 trees in four populations with distinct ecological and \ndemographic characteristics were surveyed and sampled for \nisozyme analysis. These trees were mapped for later \nplotting and spatial autocorrelation analysis. Seven \nvariable and three monomorphic allozyme loci were resolved. \nVisual examination of the distribution of single alleles \nover space revealed pattern in 22.2 percent of the 36 \nplots. Spatial autocorrelation analysis resulted in \ncalculation of 313 Moran's I autocorrelation coefficients, \n8.9 percent of which were significant (95 percent \nconfidence level). In addition, results of tests of the \ncorrelograms constructed from these 313 coefficients \nrevealed that 38.9 percent of the correlograms were \nsignificant using Bonferroni's criterion (90 percent \nconfidence level). These results suggested that the null \nhypothesis be rejected for a modest, but still important \nproportion of the tests. The spatial pattern that was \nobserved, both by eye and through statistical tests, was \nmanifested mainly as a tendency for similar genotypes to \noccur in trees that were near to each other.
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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 | 0.001 |
| 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".