Bibliographic record
Abstract
In recent years there has been increasing concern raised over concentrations of nitrates and pesticides in both surface and ground water within the Great Lakes Basin. Increases of concentration may possibly be associated with the migration of these materials from agricultural lands (Agriculture Canada 1992). While a soil nitrogen test for corn has been developed, most producers determine their rates of nitrogen fertilizer based solely on economic factors, expected yield and the price ratio of nitrogen to corn. Successful farming practices, as measured by high economic yields, are also important environmentally. Crops take up nitrogen derived both from mineralization of soil organic matter and fertilizer sources. Nutrients not taken up by the crop remain potentially available to be lost by leaching or volatilization. Our hypothesis is that matching nitrogen application rates more to crop utilization will result in less opportunity for leaching inorganic nitrogen, no matter what its source to ground water. Eventually, this should lead to reduced nitrate concentrations in ground and surface water. Certain weed species have developed triazine pesticide tolerance in Ontario. There is also evidence that selective soil bacteria adapt to use applied pesticides as convenient energy sources; such soils are said to have enhanced degradation capability. Annual application of the same
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.213 | 0.077 |
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".