High-resolution temperature logging to support ore systems research in dynamic hydrogeological settings
Bibliographic record
Abstract
Abstract Borehole temperature logging has the potential to provide insights into the presence and origin of uranium deposits; however, subtle radiogenic signatures from deposits may be disrupted by flow between different hydrogeologic units in open boreholes. A methodological study is underway at the Geological Survey of Canada’s Deep Bedrock Borehole Calibration Facility in Ottawa, Ontario, to assess the influence of groundwater flow in open boreholes on the interpretation of geothermal and radiogenic effects. Measurement techniques include high-resolution, single and multi-sensor temperature logging, flowmeter testing, repeat thermal recovery logging, and temporary installation of pressure and temperature sensors behind a liner for 6 months. In open, cross-connected boreholes, environmental (seasonal) thermal influences were observed to extend to 180 m in depth, but once vertical flow was eliminated with a liner, the hetero-homothermic boundary was interpreted to lie between 40 and 50 m. Horizontal thermal gradients were observed to change at different horizons, contributing to the conceptualization of hydrogeological units at the site. Flow magnitude (0.0–3.3 l/min) and direction were observed to be influenced by deeper hydraulic pressures triggered by precipitation or snowmelt events. As a result, the identification of dynamic conditions required repeat and/or continuous monitoring. Although site-wide temperature patterns are similar, variation on the order of hundredths to a few degrees Celsius between wells is observed, influenced by each borehole’s intersection with a complex fracture network. Experimentation and observations at the test site led to the development of a temperature-logging methodology suited for deep, narrow-diameter exploration boreholes.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".