Herbage response to precipitation in central Alberta boreal grasslands
Bibliographic record
Abstract
The dependence between grassland herbage production and precipitation within the Boreal region of central Alberta was evaluated. Additional objectives were to compare current year growing season (e.g., April or May, to August) precipitation with 12 and 16 month water year (e.g., dormant and growing season) precipitation for use in predicting herbage growth, and determine whether lowland and upland grasslands differ in their response to precipitation. Lowland herbage production averaged 6,053 kg ha(-1), nearly twice the 3,153 kg ha(-1) found on upland grasslands during the study. In general, herbage production correlated significantly with precipitation, but the magnitude and direction of that relationship varied depending on grassland type. Uplands displayed a positive linear relationship with precipitation (r = 0.76; p < 0.01), while lowland communities displayed a negative curvilinear (R2 = 0.65; p < 0.05) relationship. Furthermore, while herbage production on uplands was better predicted by current year precipitation, lowland production appeared more heavily dependent on precipitation falling during the water year, the latter of which included fall and winter moisture recharge. We hypothesize that these differences are linked to water redistribution within the landscape, along with subsequent soil temperature regimes and the length of effective growing season. Given the influence of topography in regulating water availability and use, rangeland managers within the Boreal region should use caution when determining rangeland carrying capacity from meteorological data.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".