Copper and zinc amounts and distribution in soil as influenced by application of animal manure in east-central Saskatchewan
Bibliographic record
Abstract
Increasing use of animal manures in Saskatchewan requires information on the fate and distribution of residual manure Cu and Zn in Saskatchewan soils. To address this issue, the amounts of soil Cu and Zn in various inorganic and organic fractions were investigated in a field crop research plot (Cudworth Association soil) with a 5-yr history of annual application of liquid swine manure and solid cattle manure, and in two grassland field research plots (Meota and Oxbow Association soils) that had received annual application of liquid swine manure for 3 yr. The annual rates of manure application were based on N contents in the manures, and were equivalent to approximately 0, 100, 200 and 400 kg total N ha-1 yr-1 in the field crop plots, and 0 and 100 kg total N ha-1 yr-1 in grassland plots. In both the field crop and grassland manured plots there were no substantial increases in total Cu an d Zn in soils associated with manure application. Some increases in the moderately labile Cu and Zn fractions were observed in treatments with large amounts of animal manures applied every year. The liquid swine manure had less effect than cattle manure on increasing labile Cu and Zn fractions. These results indicate that annual addition of animal manures at rates of approximately 100 kg N ha-1 for 3 to 5 yr does not constitute an environmental risk from Cu and Zn loading in these soils. Key words: Cu fraction, Zn fraction, sequential Cu extraction, sequential Zn extraction, urea, swine manure, cattle manure
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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 | 0.001 | 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".