Visualization of geologic geospatial datasets through X3D in the frame of WebGIS
Bibliographic record
Abstract
3D geo spatial data have become the normal. However, to view the data, usually expert software is required, which have up to now hindered the wide spread use of 3D scenes for the display of geological data. The internet real time 3D rendering framework X3D is assessed regarding its suitability for building a geological GIS on the internet. Especially important for geological data, 3D rendering enhances the intuitive grasp of the data and enables the user to interactively explore it. It is often necessary to find a solution to distribute this data to a wide range of interested parties, experts and non-experts alike. According to the nature of 3D data, the best technique to display geo-data, the modeling of objects and unresolved issues have to be taken into consideration. The internet is the apparent tool for the public distribution and visualization of 3D data and it was found that through the open ISO-standardized format X3D it offers a multitude of possibilities. A 3D geological interactive map was created with these prerequisites to identify challenges and possibilities through this process. It was found that the use of lead to satisfactory results, that could probably not have been achieved with another technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".