Geophysical data interpretation for the York University ATES site investigation, Ontario
Bibliographic record
Abstract
Aquifer Thermal Energy Storage (ATES) systems have the potential of reducing heating and cooling energy consumption at institutional and commercial scales. ATES systems are popular in Europe, particularly in areas of extensive glacial and post glacial unconsolidated sediment. Southern Ontario shares numerous similarities with such settings. To support an ATES study at York University, Toronto, Ontario, three geophysical datasets were collected i) Microtremor analysis (the horizontal-to-vertical spectral ratio technique, HVSR), ii) seismic reflection, and iii) borehole geophysics. The three techniques provide different scales and resolution of subsurface investigation and form a complementary suite of tools. In areas with thick sediment cover, depth to bedrock estimations often suffer from sparse data. The HVSR technique is a low cost, nonintrusive, rapid approach to estimating depth to bedrock. ATES systems commonly require enhanced information on the succession of surficial geological units, and aquifer geometry and heterogeneity. Seismic reflection data collection can provide insights into all these characteristics and consequently provide greatly enhanced target information for follow-up drilling. The confidence in seismic interpretation can be improved through collection of subsurface information from drilling, either through the combination of drill core logging (sedimentology), core testing, and downhole geophysics. Multiple downhole geophysical data were collected to support i) lithological characterisation (gamma, conductivity, magnetic susceptibility), ii) seismic velocity analysis (p and s-wave), and iii) hydrogeological characteristics (temperature, and porosity using nuclear magnetic resonance). Collectively, the geophysical data can be framed in a basin analysis methodology. This study shows that these surveys can reduce uncertainty - and potentially the cost - of mitigating a poorly understood geological context that could compromise the full potential of an ATES development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
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".