Integrated Hydrogeophysical Inversion L. R. Bentley (University of Calgary),
Bibliographic record
Abstract
Managing groundwater resources and remediating contaminated groundwater require mathematical models to predict groundwater fluxes, quantify groundwater volumes and chemical transport. The models require definition of the physical system geometry, boundary conditions and specification of the values of parameters such as hydraulic conductivity. All of these parameters always are known with significant uncertainty because data are limited in space and time and often also have uncertainty associated with them. Hydrologic targets of primary focus for the meeting will be the rates and pathways of water flow, chemical transport and degree of water storage in the subsurface. These processes are dynamic and occur at a wide range of spatial scales. Also, as with most subsurface hydrologic processes, spatial heterogeneity of the hydraulic properties must be accounted for in quantitative analysis. Typically, the challenge for quantifying these processes lies in a severe lack of temporal and spatial to describe complex systems. Geophysical methods based on physical principles including electrical, electromagnetic, seismic, nuclear magnetic resonance, and gravity have been used to assess hydrologic parameters and processes. Geophysical methods are useful because properties such as electrical conductivity can be correlated to hydrogeologic parameters such as moisture content and hydraulic conductivity Typically, the challenge for geophysical interpretation lies in the underconstrained nature of the data sets, leading to uncertain interpretations. Coupled with limitations to the petrophysical models that relate
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".