Long-term ecological behaviour of abandoned uranium mill tailings : 1. Synoptic survey and identification of invading biota / for Environment Canada
Bibliographic record
Abstract
Inactive uranium mill tailings were surveyed in the Province of Ontario to describe their surface characteristics, identify naturally invading biota, and determine essential chemical and physical parameters associated with the tailings. Inactive tailings sites can have wet areas, tailings completely covered with water, and dry areas. In the wet areas of most sites, wetland vegetation stands were found which were dominated by species of cattails (Typhaceae), along with some species of rushes (Juncaceae) and sedges ( Cyperceae). Dry areas of the tailings exhibited a variety of surface features which are often a reflection of different amelioration efforts. Most of the indigenous species of vascular plants identified on dry areas of the tailings occurred only sporadically. Invading plants found on most sites were the tree species, trembling aspen (Populus tremuloides) and paper birch (Betula papyrifera). Elemental concentration and some physical characteristics of the tailings collected from a depth of 0-20 cm were determined. The concentrations of calcium and uranium were found to have extremely large variations and cannot be considered typical for the tailings material of a site or an area (Bancroft and/or Elliot Lake). Concentrations of aluminum, copper, cobalt, magnesium, manganese and nickel in the tailings were less varied. In all tailings materials from the Bancroft area, concentration ranges of the above elements were similar to those of soils collected from both areas and to the values quoted in literature. The elements sodium and lead are possibly enriched in the tailings of the Bancroft area compared to mineral soils from the same area, and to soils in general. For the tailings in the Elliot Lake area, aluminum, chlorine, magnesium and nickel were determined in concentration ranges typical of soils, but manganese and sodium were depleted in the surface of those tailings. Lead concentrations in the tailings were elevated in this area. Thus, considering concentrations of selected elements in tailings, differences between the mining areas are evident. A comparison of pH, electrical conductivity, loss-on-ignition and moisture loss of tailings with that of soils, suggests that tailings differ drastically in these characteristics. These results are important with respect to natural rehabilitation of inactive or abandoned uranium mill tailings. Uptake of heavy metals and radionuclides was evaluated in trees found in the dry areas and in cattails (Typha latifolia) in the wetland areas. In trees, the metals and radionuclides were distributed throughout all organs whereas in the cattails, a large fraction was retained in the roots. Concentration factors (plant/tailings concentrations) were found to be generally below unity, with some exceptions for radium-226 in the cattails. Water bodies on tailings and surface water leaving the tailings, before and after treatment, were characterized in this survey. The surface waters on the tailings and seepages were usually acidic; however, the pH was not lower than that of most waters leaving natural acidic environments (bogs or muskegs). The treatment of the effluent water was found to reduce production of biomass as estimated by determination of chlorophyll a. Radionuclide and metal concentrations in waters associated with tailings were found to have extremely large variations. Detailed work is required to determine the factors controlling the physical and chemical characteristics of surface waters on tailings. Aquatic_ bryophytes have invaded some water bodies on the tailings, and acid tolerant algae were evident in most of the water associated with the tailings. The promotion of growth of these biota together with the wetland communities on the tailings beaches may provide some means of natural amelioration for the wet areas of the inactive tailings sites in the long-term. Some inactive tailings sites, described in this survey, were completely covered with water and wetland vegetation. The interactions of these biota with the tailings could be investigated on these sites. Ecological processes occurring on inactive uranium mill tailings which were identified in this survey are essential in evaluating the long-term fate of these waste sites.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".