Long-Term Benefits of Controlled Drainage
Bibliographic record
Abstract
Highlights Controlled drainage increased grain yields in drier than normal years. Monthly precipitation has a strong influence on crop yields. In normal and wetter than normal years, controlled drainage could be detrimental to crop yields. Controlled drainage generally reduced nitrous oxide emissions and leaching of nitrates in drain outlets. Abstract. Conventional tile drainage is essential for crop production in Eastern Canada. It is of increasing interest for cereal and grain producers, especially in light of potential agronomic and environmental benefits. Based on data collected over a 13-year period at an intensive maize production site in southern Quebec, we found that controlled drainage has an overall positive effect on grain yield. In some years, depending on precipitation intensities and timing, nitrous oxide (N2O) fluxes under controlled drainage were greater than under conventional tile drainage. In other years, controlled drainage had 43% lower N2O emissions. Controlled drainage significantly reduced NO3-N contamination and tended to increase crop yield. It can be a beneficial management practice to be adopted on subsurface-drained croplands. Keywords: Controlled drainage, Crop yield, Environment, Greenhouse gases, Subsurface drainage, Water quality.
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.
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 | 0.000 | 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) | 0.002 | 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".