Impact of Ice Ages on the genetic structure of trees and shrubs
Bibliographic record
Abstract
Data on the genetic structure of tree and shrub populations on the continental scale have accumulated dramatically over the past decade. However, our ability to make inferences on the impact of the last ice age still depends crucially on the availability of informative palaeoecological data. This is well illustrated by the results from a recent project, during which new pollen fossil maps were established and the variation in chloroplast DNA was studied in 22 European species of trees and shrubs. Species exhibit very different levels of genetic variation between and within populations, and obviously went through very different histories after Ice Ages. However, when palaeoecological data are non-informative, inferences on past history are difficult to draw from entirely genetic data. On the other hand, as illustrated by a study in ponderosa pine, when we can infer the species' history with some certainty, coalescent simulations can be used and new hypotheses can be tested.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".