Potential impacts of climate change on grazing capacity of native grasslands in the Canadian prairies
Bibliographic record
Abstract
Relationships between climate and native grassland production in the Canadian prairies were modelled and used to estimate the potential impacts of climate change on grazing capacity. Field measurements of production were related to climate variables and water balance estimates using regression analysis. Historical time series showed that year-to-year production is most closely correlated with annual actual evapotranspiration, whereas geographic patterns revealed that average production is most closely related to the annual water deficit. Climate and production estimates from the US Great Plains represent potential analogues for the Canadian prairies in the 2050s. Analysis of geographic patterns using Canadian and US data showed that production can be related to actual evapotranspiration (Model 1) or the ratio of actual to potential evapotranspiration (Model 2). The proportion of warm-season (C4) grasses has a significant effect on production in these models. A third independent model (Model 3) using US production data was used for comparison. Five general circulation model (GCM) scenarios covering a range of predictions simulated warmer climates of the 2050s. The production models were used to estimate changes in grassland production. On loamy soils, Model 1 predicts increases in production whereas Models 2 and 3 predict decreases. However, all predicted changes are modest, indicating that Canadian grasslands will probably remain productive over the next 50 yr. In addition, warm-season grasses could increase, particularly on sandy soils, thus benefiting productivity. Key words: Climate change, grazing capacity, grasslands, prairies
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".