Divergent Norms of Reaction to Temperature in Germination Characteristics among Populations of the Arctic-Alpine Annual, <i>Koenigia islandica</i>
Bibliographic record
Abstract
The timing of germination is a critical life-history trait for annual plants because it coordinates development from seedling to reproduction with the growing season. Arctic/alpine annuals are subject to the constraints of an especially brief growing season in which temperature both acts as a germination cue and limits growth. The evolution of phenotypic plasticity in response to temperature is thus expected, measured as norms of reaction. Strong selection in combination with differences among habitats in appropriate temperature cues leads to the expectation of population divergence in norms of reaction. Their form may be complex; no a priori mathematical distribution can be assumed. Here, we use nonparametric smoothing (Loess) to detail norms of reaction of germination to temperature, and find significant genetic divergence among six widely distributed arctic and alpine populations of the annual Koenigia islandica from Norway, the U.S.A., and Canada. Germination plasticity is discussed in relation to temperature regimes recorded under field conditions; however, any adaptive inference based on lab observations must be interpreted cautiously. The present results indicate strong population differentiation in plasticity, and highlight the necessity to consider evolved differences in life-history traits when evaluating the vulnerability of arctic and alpine species to a changing climate.
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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 | 0.000 | 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 | 0.001 |
| 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".