Bibliographic record
Abstract
Limber pine (Pinus flexilis) is one of the North American white pines under duress. Pressured by several forces including an introduced fungal pathogen (Cronartium ribicola) and changing climate, this is a species of conservation concern in many regions. The ability to adapt to shifting conditions depends on the amount and distribution of standing genetic variation. To evaluate the patterns and extent of variation in limber pine, I examined needle traits with regards to pathogen infection, and the distribution of quantitative traits in the context of climate. Specifically, I measured how leaf traits differ after surviving an inoculation with C. ribicola and found that survivors of infection had significant differences in needle size, stomatal density and specific leaf area compared to uninoculated controls. In addition, the variance of each trait shifted modestly, pointing to signs of both phenotypic selection and plasticity. Next, I examined the structure of quantitative genetic variation across 16° of latitude by phenotyping traits in a common garden experiment. This trial revealed that population differences explained between 1-24%, and family between 1-20% of the total phenotypic variance, depending on the trait under inspection. This corresponded to a mean Qst estimate of 0.158 (range 0.02-0.19), with growth traits exhibiting the greatest population differentiation. Precipitation-related climate variables were the strongest predictors of differences among populations. These results suggest that limber pine has relatively low levels of quantitative genetic variation among populations, but an almost equivalent amount within populations. Whether or not it will be sufficient to cope with the many stresses this species contends with remains unclear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".