Geoscience tools for supporting environmental assessment of metal mining
Bibliographic record
Abstract
The goal of this activity is to test the hypothesis that climate variability controls metal(loid) cycling in the environment. We initiated research in 2015-16 to provide missing baseline geochemical data and model the cumulative impacts of geogenic and anthropogenic processes, with a focus on climate variability, on the transport and fate of metal(loids) in the vicinity of the City of Yellowknife, Northwest Territories. Due to the complex geology of the Slave Geological Province and in particular, mineralized greenstone belts and hydrothermal alteration zones, geochemical background can be highly variable even on small spatial scales. In addition, the Yellowknife region has experienced ~75 years of gold ore mining and processing that resulted in release of substantial quantities of arsenic to the surrounding environment. The larger POLAR Knowledge Canada S&T funded activity will also focus on the Courageous Lake area that is thought to have been impacted by free-milling gold mining and processing at Tundra, Salmita, and Bulldog mines in the 1960s and 1980s, and the yet to be developed Hope Bay area (TMAC Resources Ltd.) in the central and northern Slave Geological Province, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".