Bibliographic record
Abstract
Thermal, hydraulic and mechanical properties of rocks play a key role in geothermal-related research. Information about these properties is essential for the characterization of the subsurface and the parametrization of numerical models. Assessing the geothermal potential of a target site relies on the accurate knowledge of these reservoir properties. Furthermore, knowledge of the thermal, hydraulic and mechanical properties of reservoir rocks is crucial in the assessment of the technical feasibility of geothermal technologies and are the basis for an economic reservoir assessment. As geothermal exploration grows in Canada, more subsurface information is becoming available that can help the development of future projects in areas with similar geological context but lacking data. In this context, a database was developed in Borealis Dataverse containing information about thermal and mechanical properties of core samples from different physiographic regions of Canada (i.e., Canadian Shield and Western Cordillera). The database also contains information about mineralogy and geochemistry of the rock samples. In this presentation and accompanying paper, we introduce the database, provide a description of the laboratory analyses performed, and present an overview of the data released for future research. The applicability of the data (e.g., heat flow assessments, parametrization of numerical models) and the advantages and limitations of this database for the end user will also be discussed. An important objective will be to keep this database updated as new information becomes available.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.093 |
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".