Review of the state of practice in geovisualization in the geosciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Geosciences modelling and 3D geovisualization is growing and evolving rapidly. Driven by commercial urgency and an increase in data from sensor-based sources, there is an abundance of opportunities to analyze geosciences data in 3D and 4D. Geosciences modelling is developing in GIS based systems, 3D modelling through both game engines and custom programs, and the use of extended reality to further interact with data. The key limitations that are currently prevalent in 3D geovisualization in the geosciences are GIS representations having difficulty displaying 3D data and undergoing translations to pseudo-3D, thus losing fidelity, financial and personnel capital, processing issues with the terabytes worth of data and limited computing, digital occlusion and spatial interpretation challenges with users, and matching and alignment of 3D points. The future of 3D geovisualization lies in its accelerated growth, data management solutions, further interactivity in applications, and more information regarding the benefits and best practices in the field.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it