Dissolved Phosphorus Losses in Tile Drainage under Subirrigation
Bibliographic record
Abstract
Abstract Recent studies have shown subirrigation (SI) to be effective in reducing nitrate losses from agricultural tile drainage systems. A field study was conducted from 2001 to 2002 in southwestern Québec to evaluate the effect of SI on total dissolved phosphorus (TDP) losses in tile drainage. In an agricultural field with drains installed at a 1-m depth, a SI system with a design water table depth (WTD) of 0.6 m below the soil surface was compared with conventional free drainage (FD). Subirrigation increased drainage outflow volumes in the autumn, when drains were opened and water table control was interrupted for the winter in the SI plots. Outflows were otherwise similar for both treatments. Throughout the study, the TDP concentrations in tile drainage were significantly higher with SI than with FD for seven out of 17 of the sampling dates for which data could be analyzed statistically, and they were never found to be lower for plots under SI than for plots under FD. Of the seven dates for which the increase was significant, six fell in the period during which water table control was not implemented (27 September 2001 to 24 June 2002). Hence, it appears that SI tended to increase TDP concentrations compared with FD, and that it also had a residual effect between growing seasons. Almost one-third of all samples from the plots under SI exceeded Québec's surface water quality standard (0.03 mg TDP L-1), whereas concentrations in plots under FD were all below the standard. Possible causes of the increase in TDP concentrations in tile drainage with SI are high TDP concentrations found in the well water used for SI and a higher P solubility caused by the shallow water table.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".