Corn Yield Response to Drainage and Subirrigation in the Canadian Prairies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Subsurface drains are commonly used in humid regions to deal with high water tables. However, corn (Zea mays L.) could benefit from subsurface drainage even under semi-arid conditions where high-intensity rainfall causes the water table to rise within the root zone for short periods. In southern Manitoba, seasonally high water tables with high salinity have led to salinization of the root zone, making subsurface drainage an attractive option to increase yields. The objective of this research was to evaluate agronomic performance of corn under water table management using subirrigation and tile drainage. Four treatments were tested in this experiment: (1) controlled drainage with subirrigation (CDSI), (2)no drainage with overhead irrigation (NDIR), (3) free drainage with overhead irrigation (FDIR), and (4) no drainage with no irrigation (NDNI) as control. The impacts of these treatments on crop performance, measured by yield, kernel quality, plant biomass, and plant height, were evaluated over two growing seasons. In the first year, which was 57% wetter than the 30-year average, yields were 8.48 (NDNI), 10.36 (NDIR), 10.10 (FDIR), and 9.22(CDSI) Mg ha-1 with only the mean yield difference for the NDIR and the CDSI treatments being statistically significant (p = 0.014). In the second year, which was 16% drier than normal, yields were 9.25 (NDNI), 10.47 (NDIR), 11.28 (FDIR), and 9.49 (CDSI) Mg ha-1 with no statistically significant differences in yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it