Comparative nitrogen partitioning and water use by native and introduced grass communities in southern Alberta, Canada
Bibliographic record
Abstract
The objectives of this research were to evaluate 1) short-term changes in soil and plant N partitioning created by cultivating and re-seeding native grasslands with two cropping systems of wheat and perennial (crested wheatgrass and Russian wildrye) monocultures; 2) differences in the rate of soil water uptake between Mixed Prairie grasslands, crested wheatgrass and Russian wildrye after a dry-down period; and 3) differences in above ground water use efficiencies, root and crown masses between Mixed Prairie grasslands, crested wheatgrass and Russian wildrye under two different soil water contents. The perennial agronomic species were recommended by Agriculture and Agrifood Canada for seeding in Mixed Prairie and Fescue grassland in southern Alberta, Canada. In the first four years after plow-down, soil nitrate (NO3 -) concentration was higher and light fraction N (LFN) was lower in the soil under wheat than native grasslands. Although LFN was lower in perennial monocultures than native grasslands, there was little difference in soil nitrate. More N was partitioned into shoot biomass of wheat, crested wheatgrass and bromegrass that native grasslands and levels increased as annual and long-term growing season precipitation increased. There were no differences in the rate of soil water uptake after dry-down periods between native Mixed Prairie, crested wheatgrass or Russian wildrye, but both perennial monocultures had higher above ground water use efficiencies than native Mixed Prairie.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".