Forêts et terres agricoles : un partenariat mutuellement avantageux favorisant l’économie circulaire
Bibliographic record
Abstract
Forest and farmland: A mutually beneficial partnership with an eye on the circular economyFarmers have many ways to help ensure the soil on their farmland is healthy, like reducing tillage, minimizing nutrient loss (i.e.nitrogen and phosphorus), and planting cover crops.However, some farmers have discovered another helpful tactic-applying paper mill biosolids-known to benefit both soil and agricultural crops.To learn more about the effects of paper mill biosolids application on soil and plants, scientists from Agriculture and Agri-Food Canada (AAFC) have studied this practice over the past 20 years, and the results are showing this could be the beginning of a beautiful friendship.Paper mill residues pose both significant challenges and interesting opportunities.Every year, the forest industry in Canada generates 1.5 million dry tons of paper mill biosolids from treated liquid waste, along with an additional amount from alkaline by-products such as wood ash and lime mud.Most of these are sent to landfill.However, if used efficiently as soil amendment, these residues can decrease the need for synthetic fertilizers, which would reduce costs and increase the environmental sustainability of agricultural production.To study this more, AAFC scientists established an experimental site in 2000 in the Mauricie region of Quebec.It became the only site in Canada dedicated to studying long-term repeated applications of different residues from the paper industry.From 2000 to 2021, scientists conducted many studies on various issues including crop yields, and physico-chemical and biological characteristics of soil.The findings showed that paper mill biosolids can improve soil quality by increasing the organic matter content and the availability of major nutrients (phosphorus, nitrogen, potassium, calcium, and magnesium), and by reducing soil acidity.When applied at a rate of less than 60 wet tons/ha, biosolids pose little risk
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".