Introduction to Traditional Knowledge studies in support of geoscience tools for assessment of metal mining in Northern Canada
Bibliographic record
Abstract
This report describes a three-year project (2015–2018) to improve understanding of past climate. It studies the way metal(loids) have responded to different climate conditions in the past. Lake sediments and peatlands are known as paleoecological archives. In other words, they contain fossils that help understand the past ecology of plants and animals in the area. The study looked at historical climate conditions shown by the chemistry and fossil content of these sediments and peatlands. It also examined Inuit Qaujimajatuqangit (traditional knowledge). This may result in more accurate predictions of how metal(loids) respond to current and forecasted climate change. The Yellowknife and Courageous Lake areas of the mineral-rich Slave Geological Province have gold mining histories. These areas were studied to see how metal(loids) mobilized by past mining activity may move through the environment. The project partners—the North Slave Métis Alliance, the Yellowknives Dene First Nation, the Tłįchǫ Research and Training Institute, and Hadlari Consulting Ltd.—conducted Traditional Knowledge and Inuit Qaujimajatuqangit studies. Information from these studies will be used together with data collected by western scientific techniques. This will provide a better understanding of the transport and fate of metal(loids) under a changing climate.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".