Can Ammoniacal Nitrogen from Gold Mining Effluent Be a Promising Alternative for Fertilizing Boreal Forest Stands?
Bibliographic record
Abstract
Nitrogen is an essential nutrient for plant functioning, photosynthesis, and metabolic activities. In terrestrial settings, nitrogen is not always sufficiently available because its basic form (N2) must be fixed into other forms, such as nitrate and ammonium, to be usable by plants. Adding nitrogenous fertilizer to soils may provide a means of increasing forest productivity. Ammoniacal nitrogen (N-NH3), an effluent produced during gold extraction, requires mining companies to manage its long-distance and costly transportation offsite for disposal. Applying this nitrogenous effluent, in its treated form of ammonium sulfate (ammoniacal nitrogen from mine water was converted into ammonium sulfate locally), to regional forest stands could provide a cost-effective and more environmentally sound means of managing this waste product and enhance forest productivity. Here, we conducted greenhouse- and field-based experiments to evaluate ammonium sulfate fertilization on black spruce (Picea mariana) and jack pine (Pinus banksiana) seedling growth. We assigned five treatments, varying in terms of the fertilizer concentration and presence/absence of biochar, to seedlings in greenhouse trials. We also applied various concentrations of ammonium sulfate to an 8-year-old black spruce plantation in Abitibi-Témiscamingue, Québec. We found that black spruce and jack pine seedlings experienced greater growth than the controls in terms of the stem diameter (32–44%), seedling height (21–49%), and biomass (86–154%). In the field experiment, we observed 37% greater volumetric growth in plots receiving medium-level fertilization than the control. Although nitrogen fertilization lowered the soil pH, essential nutrients increased to favor greater seedling growth. Thus, ammonium sulfate, derived from local mining effluent, appears to offer a suitable alternative for enriching nitrogen-limited boreal soils and increasing tree growth. This application could benefit both regional mining industries and forest management bodies.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".