Bibliographic record
Abstract
For almost two decades, the Geological Survey of Canada has been working to understand the effects of geological resource development on the environment. This research is supported by the Environmental Geoscience Program. The goal of this program is to provide leading-edge scientific information to differentiate the effects of natural resource development on the environment from those of natural processes. The development of new geoscientific approaches serves to support the responsible development and use of Canada's natural resources through informed decision-making.In this presentation, we will take a brief look back at key past activities and focus on the series of new projects that have just begun.From 2019 to 2024, the program conducted some 15 projects in the following five themes: Baseline Characterization, Cumulative Effects, Deep Environments, Emerging Issues and Biosphere, Hydrosphere, Atmosphere. The range of projects included, among others, induced seismicity, oil sands, geological carbon sequestration and global mercury assessment with UNEP.In its current phase (2024-2029), the program comprises a series of fourteen projects, divided into 4 themes: impact assessment, regional assessments, processes and characterization. Current projects include topics as varied as the national integration of groundwater knowledge, the use of clumped isotopes to characterize nuclear waste disposal sites, the study of metals in the environment of active metalliferous regions, or the study of aquifer contamination by legacy oil and gas wells on indigenous lands.The vastness of the Canadian territory, combined with a resource-rich subsoil, provides the opportunity to carry out a multitude of geoenvironmental projects in support of sound environmental stewardship for the benefit of local communities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.028 |
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".