Visualization and Sharing of 3D Digital Outcrop Models to Promote Open Science
Bibliographic record
Abstract
High-resolution 3D data sets, such as digital outcrop models (DOMs), are increasingly being used by geoscientists to supplement field observations and enable multiscale and repeatable analysis that was previously difficult, if not impossible, to achieve using conventional methods.Despite an increasing archive of DOMs driven by technological advances, the ability to share and visualize these data sets remains a challenge due to large file sizes and the need for specialized software.Together, these issues limit the open exchange of data sets and interpretations.To promote greater data accessibility for a broad audience, we implement three modern platforms for disseminating models and interpretations within an open science framework: Sketchfab, potree, and Unity.Web-based platforms, such as Sketchfab and potree, render interactive 3D models within standard web browsers with limited functionality, whereas game engines, such as Unity, enable development of fully customizable 3D visualizations compatible with multiple operating systems.We review the capabilities of each platform using a DOM of an extensive outcrop exposure of Late Cretaceous fluvial stratigraphy generated from uninhabited aerial vehicle images.Each visualization platform provides end-users with digital access and intuitive controls to interact with large DOM data sets, without the need for specialized software and hardware.We demonstrate a range of features and interface customizability that can be created and suggest potential use cases to share interpretations, reinforce student learning, and enhance scientific communication through unique and accessible visualization experiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".