Annual rings of perennial forbs and mature oaks show similar effects of climate but inconsistent responses to fire in the North American prairie–forest ecotone
Bibliographic record
Abstract
For the prairie–forest ecotone of central North America, research of the effects of climate and fire on the annual growth of nonwoody plant types is currently needed to compliment dendrochronological research used for predicting the stability of this ecotone in the future. Using cores of Quercus stellata Wangenh. and collars of taproots of Asclepias viridis Walter from central Oklahoma, as well as cores from Quercus macrocarpa Michx. and Lespedeza capitata Michx. collars in central Minnesota, we aimed to distinguish the response towards annual precipitation and temperature, as well as fire regime, between these co-existing plant types through patterns in annual ring growth. The effect of spring fire on the annual growth increment was only consistently significant for one of the forbs, with a positive relationship. The strong negative effect of summer temperature was consistent between forbs and Q. stellata, while the positive effect of either growing season precipitation was consistent between plant types. Furthermore, we found stronger patch-specific annual ring patterns in forbs in comparison with trees when patches are separated based on unique fire histories. Overall, such efforts could be used in further studies to better predict growth rates of dominant plant types in landscapes susceptible to significant environmental change.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".