Finding the sweet spot : advancing Evaplant technology for mine reclamation in Canada’s north and west
Bibliographic record
Abstract
Since its introduction at TRCR in 2024, Evaplant technology has advanced as a nature-based, zerodischarge solution for treating contact water using hybrid willow evapotranspiration. New feasibility studies in Quebec and across British Columbia have confirmed the applicability of Evaplant in diverse geoclimatic conditions. Feasibility studies in BC in particular span from the BC South Interior to the North-Central plateau, indicating broad viability. This paper presents a year’s worth of growth and evolution of the Evaplant system, from its description and introduction to TRCR attendees in 2024. Updated modeling for the BC Central Coast, Cariboo, and Omineca-Peace regions, incorporating climate and evapotranspiration factors, indicates suitability with similar or better water management rates than sites in Northern Quebec. It also details how willow biomass is being used for soil priming, peat moss replacement, and nursery input in reclamation supply chains. Preliminary cultural feedback from Indigenous collaborators has identified the willow as a culturally resonant, carbon-sequestering tree, reinforcing social license potential. The development model has been codified as a four-step process. The paper reviews new data, climate-based irrigation modeling, feasibility progression pathways, and emerging implementation models, all positioning Evaplant as a scalable solution for remote and active mine water challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".