The timing of snowmelt and amount of winter precipitation have limited influence on flowering phenology in a tallgrass prairie
Bibliographic record
Abstract
A growing body of work indicates that the timing of flowering of temperate angiosperms has been affected by shifts in climate since the 1970s. Sensitivity in flowering phenology to changing temperatures has been particularly well-documented, but widespread phenological sensitivity to changing precipitation patterns in temperate communities has only been shown in a few studies. The exception is relationships between snowpack and early flowering in alpine environments, whereby the timing of flowering herbs had strong associations with winter precipitation and the snowmelt timing. Based on these results, we hypothesized that populations in temperate latitudes characterized by strong seasonality and winter snowfall would demonstrate associations between timing of snowmelt and flowering phenology. We combined a historical dataset of first flowering dates in a Minnesota tallgrass prairie with climatic data to construct a structural equation model, testing hypotheses on the relationships between winter precipitation, temperature, and flowering phenology. While temperature had a strong effect on flowering phenology, winter precipitation had a significant relationship with only a few species. The species affected by snow were later flowering species, which is inconsistent with our prediction that winter precipitation affects early flowering phenology. These results suggest future changes in precipitation will have differing consequences depending on region.
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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 | 0.000 | 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".