Rating Introgression between Lodgepole and Jack Pine at the Individual Tree Level Using Morphological Traits
Bibliographic record
Abstract
Abstract Introgressive hybridization between species generates novel gene combinations and phenotypes. We required an accessible, objective method of rating introgression between lodgepole pine (Pinus contorta var. latifolia [Engelm.] Critchfield) and jack pine (Pinus banksiana Lamb.) for individual trees where their ranges overlap in Canada for use in another study on host species effects on resistance to an eruptive herbivore that has recently expanded its range. We adapted, simplified, and fully quantified a morphological index developed to rate introgression of pine populations and applied it to individual trees. In addition to principal component analysis (PCA), we also used discriminant function analysis (DFA), a potentially more powerful method given a priori knowledge of parent taxa, to generate introgression ratings. Among-tree variation in morphological traits and introgression was high at sites within the hybrid zone but very low at pure parent sites. PCA and DFA produced similar introgression ratings at the stand level, but ratings differed substantially for some individual trees. Certain morphological traits may be omitted from both PCA and DFA with little impact on stand-level ratings. The discriminant functions presented here are based on easy-to-measure, fully quantifiable morphological traits and can be used by other researchers to produce relative introgression ratings for lodgepole and jack pine. The approach may also be applied to other plant hybrid systems.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".