Landscape genomics predicts climate change‐related genetic offset for the widespread <i>Platycladus orientalis</i> (Cupressaceae)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Understanding and quantifying populations' adaptive genetic variation and their response to climate change are critical to reforestation's seed source selection, forest management decisions, and gene conservation. Landscape genomics combined with geographic and environmental information provide an opportunity to interrogate forest populations' genome‐wide variation for understanding the extent to which evolutionary forces shape past and contemporary populations' genetic structure, and identify those populations that may be most at risk under future climate change. Here, we used genotyping by sequencing to generate over 11,000 high‐quality variants from Platycladus orientalis range‐wide collection to evaluate its diversity and to predict genetic offset under future climate scenarios. Platycladus orientalis is a widespread conifer in China with significant ecological, timber, and medicinal values. We found population structure and evidences of isolation by environment, indicative of adaptation to local conditions. Gradient forest modeling identified temperature‐related variables as the most important environmental factors influencing genetic variation and predicted areas with higher risk under future climate change. This study provides an important reference for forest resource management and conservation for P. orientalis .
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.016 | 0.005 |
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 it