De‐Inking Paper Sludge Amendment Affects Weeds in the Presence or Absence of Herbicide in a Soybean‐Corn Rotation
Bibliographic record
Abstract
The application of de‐inking paper sludge (DPS), a C‐rich but N‐poor soil amendment, can sometimes decrease plant growth. This property needs to be investigated to develop the knowledge and management methods necessary for potential weed control. Today, there is no information on the effectiveness of using DPS to control weeds within a soybean [ Glycine max (L.) Merr.]‐corn ( Zea mays L.) rotation. The objective of this study was to determine the effects of 0, 25, or 50 Mg/ha DPS applied yearly, combined with the absence or the presence of herbicides, on weed abundance and biomass. The experimental sites were St. Augustin de Desmaures for a 4‐yr rotation sequence (soybean‐corn‐soybean‐corn) and St. Hyacinthe for a 3‐yr rotation sequence (soybean‐corn‐soybean) in northeastern Canada. Without herbicide and averaged across years, when DPS was applied at 50 Mg/ha DPS, the abundance of annual broadleaf weeds decreased by 78 to 96%, and biomass by 82 to 93%, whereas with herbicide, the abundance decreased by 66 to 100%, and biomass by 80 to 100%, at St. Augustin and St. Hyacinthe, respectively, compared with the 0 Mg/ha DPS treatment. At St. Augustin, DPS applied at 50 Mg/ha without herbicide usually decreased the annual grass weed abundance and biomass. DPS had no obvious effect on the abundance and biomass of perennial weeds. DPS amendment alone or with herbicides suppressed mainly the annual broadleaf weeds, and, to a lower extent, the annual grass weeds.
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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 | 0.000 |
| 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) | 0.001 | 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".