Overview of surficial geochemistry and indicator mineral surveys and case studies from the Geological Survey of Canada's GEM Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Geological Survey of Canada carried out reconnaissance-scale to deposit-scale geochemical and indicator-mineral surveys and case studies across northern Canada between 2008 and 2020 as part of its Geo-mapping for Energy and Minerals (GEM) program. In these studies, surficial geochemistry was used to determine the concentrations of up to 65 elements in various sample media including lake sediment, lake water, stream sediment, stream water, or till samples across approximately 1 000 000 km 2 of northern Canada. As part of these surficial geochemistry surveys, indicator mineral methods were also used in regional-scale and deposit-scale stream sediment and till surveys. Through this program, areas with anomalous concentrations of elements and/or indicator minerals that are indicative of bedrock mineralization were identified, new mineral exploration models and protocols were developed, a new generation of geoscientists was trained, and geoscience knowledge was transferred to northern communities. Regional- and deposit-scale studies demonstrated how transport data (till geochemistry, indicator mineral abundance) and ice-flow indicator data can be used together to identify and understand complex ice flow and glacial transport. Detailed studies at the Izok Lake Zn–Cu–Pb–Ag VMS, Nunavut, the Pine Point carbonate-hosted Pb–Zn in the Northwest Territories, the Strange Lake REE deposit in Quebec and Labrador as well as U–Cu–Fe–F and Cu–Ag–Au–Au IOCG deposits in the Great Bear magmatic zone, Northwest Territories demonstrate new suites of indicator minerals that can now be used in future reconnaissance- and regional-scale stream sediment and till surveys across Canada.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it