Introduction - three-dimensional geological mapping
Bibliographic record
Abstract
This workshop is the seventh in an ongoing series primarily designed to share insights among geological mappers who are maximizing the use of variable and often voluminous subsurface information in order to produce 3D models that map sediment and rock, with an emphasis on the needs of groundwater management. The first workshop in this series was held in Normal, Illinois, ten years ago this past April, (Berg and Thorleifson, 2001). This year's workshop reflects the evolving focus of the series from issues of data quality, datasets, and methods for data collection to a focus at the Geological Survey Organization level on institutional implementation of modelling standards at state, provincial, and national scales. Since the 2009 workshop in Portland two notable outcomes of the workshop series have been published. Firstly, Thorleifson et al. (2010) discussed the emergence of 3D mapping at Geological Survey Organizations (GSO). Secondly, Berg et al. (2011) edited a collection of papers on approaches to 3D geological mapping at several geological survey organizations from Europe, North America, and Australia. This year's workshop continues the trend of increased focus on work at GSOs with 11 of 15 contributions focused on this subject. The four remaining papers, however, maintain the essential link to research on methods.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.097 | 0.049 |
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".