Phytoplankton Ecology in an Oilsands End Pit Lake
Bibliographic record
Abstract
Alberta oilsands mining and extraction have produced over 1 trillion litres of tailings wastewater (AER 2021) containing several compounds of concern (Cossey et al. 2021). End-pit lakes are a low-cost, long-term proposed strategy of tailings reclamation that sequester tailings in a mined-out pit under a freshwater cap. Through dilution and biogeochemical processes, the water cap should over time develop into a functional ecosystem integrable with the local watershed (Cossey et al. 2021, Saborimanesh 2021). Established in 2012, Base Mine Lake is currently the only full-scale pilot end pit lake developed by the Alberta oilsands industry and requires further investigation to validate end pit lakes as a tailings reclamation technology (Cossey et al. 2021). The first stage of reclamation requires the development of a phytoplankton community, which serves as the base of the aquatic food web (CEMA 2012). The primary objective of this study was to characterize the phytoplanktxon community over time in BML from 2016 to 2021 to determine how community composition and abundances shift over seasons and years. Characterization used Illumina gene sequencing targeting 16S rRNA, 18S rRNA, and 23S rRNA gene amplicons, giving relative abundance data over time for phytoplankton. Cell count data was used to verify gene sequencing results. The phytoplankton community composition and diversity in Base Mine Lake was compared to those of a freshwater reservoir and a tailings pond. Analysis of gene sequencing data revealed that major genera of phytoplankton included Cryptomonas ( Cryptophyceae ), Choricystis ( Trebouxiophyceae ), Euglena ( Euglenales ), and Synechococcus ( Synechococcales ), all of which appear to exhibit seasonal blooms during 2016-2021 (Fig. 1). Sequencing analysis also indicated that Base Mine Lake and its freshwater input source Beaver Creek Reservoir shared many of the same genera but different strains/species of those genera. This suggested that the distinct conditions in each aquatic site may have selected for distinct strains. Diversity analyses of gene sequencing data revealed that phytoplankton diversity in Base Mine Lake was intermediate between that of its freshwater input reservoir and a tailings pond. There currently exists no low-cost, large-scale treatment method that fully reclaims tailings water (Cossey et al. 2021, Saborimanesh 2021). End-pit lakes retain tailings until recalcitrant compounds are degraded to near-environmental levels (Saborimanesh 2021, CEMA 2012), but further research is required before end-pit lakes can be approved as a viable reclamation technology (Cossey et al. 2021). Although research is available now on native microbial communities in tailings waters, knowledge on their contributions to an aquatic microbial food web is limited (Saborimanesh 2021). This proposed research is the first of its kind to examine the contribution of phytoplankton to end pit lake food web ecology. This will advance knowledge of end-pit lakes as a reclamation strategy in effort to reduce the environmental footprint of tailings water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".