The effect of reduced snow cover and summer drought on temperate grasslands in the southern interior of British Columbia
Bibliographic record
Abstract
Approximately 26% of the Earth’s total land area and 80% of agricultural land is composed of grasslands. Lac du Bois Grasslands Protected Area near Kamloops, BC, Canada is temperate grassland that provides recreational and economic opportunities. Due to global climate change, Southern and central British Columbia precipitation patterns are predicted to shift with less precipitation in the summer and more in the winter. Although winters are predicted to become wetter, less precipitation as snowfall could lead to reduced snowpack, which is more likely to melt earlier in the spring. Snow cover provides insulation against cold air, which helps reduce frost stress during the winter and an earlier snowmelt may lead to water shortage in the summer. Changing climate trends may lead to more frequent and intense droughts combined with increased frost stress that can negatively impact plant survival and productivity. However, it is unknown how these stress events interact with each other and if there is a positive or negative relationship. This study looked at the effects of frost stress and drought, as well as the combination of both, on biomass productivity. I found that snow removal increased exposure to more variable and more negative temperatures in the winter. Plots with established rain-out shelters showed a significant decrease in soil moisture content. Above ground biomass did not differ between plots, treatments types or the controls. Although no significant results were found in biomass production, understanding how stress events interact with each other on grassland plant communities will help predict how terrestrial ecosystems will respond in the face of global climate 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".