Phosphogypsum Stack Reclamation Using Soil Amendments and Short-Rotational Woody Species
Bibliographic record
Abstract
Phosphogypsum (PG) is a by-product of phosphorus fertilizer production that is stored in large piles (stacks). Typically, PG stack reclamation focuses on topsoil caps vegetated with grass which has limited economic return. Therefore, a study was conducted using the short-rotational tree species (Picea glauca, Populus balsamifera) to determine their potential in PG stack reclamation. Five soil amendments (compost, compost with mycorrhizal fungi, manure pellets, black earth, mycorrhizal fungi) with a control (no amendment on PG) were used in a field experiment to determine whether they would enhance the growth of the planted species. After two years, amendments had little effect on the height, diameter or biomass of either species. P. balsamifera on PG (control) was significantly healthier than in amended soils. The healthiest and most successful trees for both species were found in the control treatment. Organic amendments (compost, manure) had significantly higher nitrate and phosphorus than most of the other treatments. Black earth and mycorrhizal fungi did not affect any soil properties relative to the control. This study suggests short-rotational forestry plantations of Populus balsamifera appear to be meeting reclamation objectives for PG stacks, similar to traditional grass covers; however, the built soil profile could limit tree success in future.
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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) | 0.001 | 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".