First steps towards a workflow for 3D-models based on IIIF
Bibliographic record
Abstract
Images play an important role in archaeological research and the 3D medium is becoming increasingly popular: 3D scans of archaeological objects, geo-referenced 3D scans of structures, e.g. tombs, or reflectance transformation imaging (RTI) are increasingly likely to form one part of the digital data created in research projects or by cultural heritage institutions. 3D offers many application possibilities that can contribute significantly to knowledge acquisition. Very often, however, the resulting files are extremely large and not really suited for smooth display. The situation is further complicated by the fact that there is still no real standard for both long-term storage and dissemination of 3D images. For a data repository such as the Swiss National Data and Service Center for the Humanities (DaSCH) which provides a “living archive” where data can be accessed and searched directly by humans as well as by machines, 3D images pose a series of questions and challenges. The original 3D-models are often several gigabytes large and their key features, the geometry, texture and possibly also animation, may come in separate files. How can such data be archived properly? Which non-proprietary formats can or should be accepted? To achieve interoperability and enable annotation, the goal is to have a type of IIIF dissemination. How can this be achieved from the original 3D-models? What open source 3D-viewer should be implemented in web applications? In order to achieve the long-term goal of proper archiving of the original file(s) on the one hand, and the provision of a lightweight, interoperable, viewable and annotatable version of the 3D-model in the web application on the other hand, a process needs to be established that can be automated. I will present our first attempts of establishing such a workflow. References DaSCH Service Platform (DSP): https://www.dasch.swiss/platformcharacteristics IIIF: https://iiif.io/ IIIF 3D Technical Specification Group : https://iiif.io/community/groups/3d/tsg/ IIIF-Prezi3: https://github.com/iiif-prezi/iiif-prezi3
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.066 |
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".