Multivariate patterns of adaptive genetic variation and seed source transfer in <i>Picea mariana</i>
Bibliographic record
Abstract
A short-term retrospective test trial was carried out using 90 open-pollinated families representing 30 provenances of black spruce (Picea mariana (Mill.) BSP) from Quebec. Seedlings were transplanted on three sites along a latitudinal gradient, and eleven growth and phenological traits were measured during the second and the third growing seasons. Analyses of variance indicated for most of the traits significant differences among provenances and families-within-provenances. Principal component analysis was used to summarize the variation observed among provenances into two principal components, which accounted for 79% of the total variation for all traits. Regression models developed to relate each trait and the principal component scores to geoclimatic variables explained between 55% and 86% of the variation observed among provenances. Variation in growth traits and phenological traits appeared to be related to geoclimatic factors. The models were validated using data from a range-wide provenance test, and relative risks associated with seed source transfer were estimated. The R 2 values between the transfer risk and the provenance heights ranged from 0.02 to 0.58, whereas they were slightly lower for diameters. On average, the relative risks varied from 36% to 67%. Individual provenance values ranged from 4% to 94%. A geographic information system tool was designed to assist the forest managers in making seed transfer decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".