___________________________________________________________________________________ GEOGRAPHIC CLINES IN GENETIC VARIATION
Bibliographic record
Abstract
In risk mapping, the primary considerations are the presence of the host tree species, some measure of its density, and the distribution of the pest agent. High-density or overstocked stands are often considered to be of higher risk then stands with lower stocking levels; also important is the climatic stress on the population. This presentation shows how the predictions from a climate model can be converted to variables that may indicate the status of the stress of conifer species and their populations in the western USA and southwestern Canada. Forty-eight monthlies were derived from the basic temperature and precipitation data and fit to geographic surfaces with thin plate splines. These monthlies were then used to describe the clines of genetic variation that exist within species for growth characteristics. The mapping of clinal variation is useful in delineating seed zones and deriving seed transfer guidelines. The reverse image of such maps should indicate where the species would be under stress due to climatic conditions. For this reason, it is recommended that the monthlies and the climatic limits could be useful in risk mapping.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".