Using shallow temperature measurements to evaluate thermal flux anomalies in the southern Mount Meager volcanic area, British Columbia, Canada
Bibliographic record
Abstract
Geothermal is a clean and renewable energy resource. However, locating where elevated thermal gradient anomalies exist is a significant challenge when trying to assess potential resource volumes during early exploration of a prospective geothermal area. In this study, we deployed 22 temperature probes in the shallow subsurface along the south flank of the Mount Meager volcanic complex, to measure the transient temperature variation from September 2020 to August 2021. In our data analysis, a novel approach was developed to estimate the near-surface thermal distribution, and a workflow and code with python language have been completed for the thermal data pre-processing and analysis. The long-term temperature variation at different depths can be estimated by modelling, so that the relative difference of deducing deeper geothermal gradient anomalies can be assessed. Our proposed inversion and simulation methods were applied to calculating the temperature variation at 2.0 meters depth. The results identified a preferred high thermal flux anomalous zone in the south Mount Meager area. By combining with previous studies, the direct analysis and estimation of anomalous thermal fields based on the collected temperature data can provide a significant reference for interpretation of the regional thermal gradient variation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".