Northern dry mixed prairie responses to summer wildlife and drought
Bibliographic record
Abstract
In August 1994, wildfire burned 6,500 ha of native Dry Mixed Prairie in southeastern Alberta. The following year, a study was initiated to monitor the recovery of major plant communities. Burning was followed by 3 successive years of drought, reducing total vegetative cover by 10%. Exposed soil increased to a high of 23%, three years after the fire. Litter and grass production were reduced through 1997, with the greatest decline in 1995 when grass production on burned and unburned areas averaged 890 and 1,468 kg ha(-1), respectively. Of the major forage species, Stipa spp. and Koeleria macrantha (Ledeb. J.A. Schultes f.) were affected for a single year and Agropyron spp. 2 years by burning. Both Agropyron and Stipa abundance displayed interactions with topographic position in response to fire. In 1995, Agropyron increased on uplands with burning from 90 to 143 kg ha(-1), but decreased on lowlands from 383 to 238 kg ha(-1), a pattern repeated in 1996. In contrast, Stipa declined at both positions, but only for a single year. Where livestock grazing occurred after the fire, forage removal was greater on burned areas. Drought conditions, in combination with summer wildfire, reduced Dry Mixed Prairie range productivity and ground cover for several years and intensified livestock grazing, highlighting the need for changes in rangeland management under these conditions.
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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 | 0.000 |
| 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".