Introduction to a Special Issue on Three-dimensional Geological Mapping for Groundwater Applications
Bibliographic record
Abstract
Modern societies have increasing demands for contaminant remediation and a continued supply of potable water. This is particularly the case in the densely populated and industrialized parts of North America and Europe. Coping with the demand, however, requires optimal geologic mapping and modeling methods, and for hydrogeologists to generate improved model scenarios using the best available geological information. Unfortunately, there is often a lack of collaboration between geologists and hydrogeologists. Therefore, geological complexity and understanding is often under-represented in many groundwater models. This article introduces a special issue of the Journal of Maps focusing on three-dimensional (3-D) geologic mapping for groundwater applications. Four articles in the issue are based on papers that were presented at workshops held between 2001 to 2005. The workshops provided venues for researchers to share their expertise in constructing 3-D geological models and to discuss various geological issues pertaining to groundwater and urbanization. Primary topics discussed in the workshops and in these four Journal of Maps articles are basin analysis, data integration and management, three-dimensional geologic model construction, groundwater investigations, and communication. The four papers in this issue demonstrate different philosophical and technical approaches to the development of GIS based 3-D geological models, while the modeling approaches and results reflect various issues and data support problems inherent with model development at various scales, from large regional models, to intermediate scale municipal and county scale models, to site-specific scale.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".