Characterizing the root-associated microbial community structure after 5 years of phytoremediation on gold mine waste rock in Northern Quebec
Bibliographic record
Abstract
Gold mining has historically been known to play a significant role in Quebec's economic development and also underpins the leading position of Canada in global metal production.As a result, the environmental impact on mined lands, such as ecosystem disturbance, metal contamination, and unappealing landscapes, have become a growing concern to the local community and regulatory authorities.Reclamation on the mined areas using plants and their associated beneficial microorganisms (i.e., phytoremediation) has been regarded as a cost-effective phytotechnology that holds promise in alleviating the impact of such metalliferous mining on the soil ecosystem, restoring soil sustainability and productivity, as well as improving the appearance of the landscape.As post-mining soil is nutrient-deficient and an inhospitable environment to establish plants, hardy native plants such as alders (Alnus spp.) and boreal conifers that naturally form symbioses with plant growth-promoting microbes are frequently chosen and applied with actinorhizal and mycorrhizal inoculations for phytoremediation effectiveness.In this project, we studied a phytoremediation field trial that was grown on a waste rock slope at the Sigma gold mine, Val-d'Or, QC, since 2012.The plantation consists of two alder species, green alder (Alnus viridis I would like to express my sincere thanks to my supervisor, Dr. Charles Greer, for giving me the opportunity to work on this project.His invaluable guidance, encouragements and support have helped me face and overcome many obstacles encountered during my masters.He was always generous with his profound knowledge and with his time to answer many of my questions, giving me the best advice and inspiring me to have a positive outlook on difficult situations.I do not think I could have had a better supervisor.I would also like to thank my co-supervisor, Dr.
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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 | 0.001 | 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".