Bibliographic record
Abstract
"Excess water" conditions develop when a soil is unable to transmit water, leading to the onset of saturated conditions harmful to soils and crops. Negative agricultural impacts include reduced trafficability, physical damage to crops under hypoxic or anoxic conditions, increased salinity or sodicity, reduced nutrient availability and uptake, and increased incidence of weeds and pests. There are two main objectives in managing landscapes prone to excess water, both of which must consider soil and landform characteristics. The first is to maximize infiltration and conductivity through tillage and residue management. The second is to remove water from the soil profile as quickly as possible through drainage or the adoption of high water use plants such as alfalfa (Medicago sativa), which increase water losses through transpiration. Changes to the overall cropping system can also be made, including selecting crops and forages that have shown reduced sensitivity to excess water, applying seed treatments that encourage the development of water-tolerant traits, timing fertilizer application to correspond with maximum plant uptake, and incorporating high water use crops into the rotation. Recent work from Australia suggests that management of excess water requires a multi-disciplinary approach; however, little research has been done on this problem in the semiarid to sub-humid Canadian Prairies. Key words: Waterlogging, beneficial management practices, infiltration, drainage
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".