A framework for repurposing multimedia content
Bibliographic record
Abstract
Nowadays, there are a lot of different device profiles (desktop PC, Web TV, handheld PDA, Internet screen telephones, cellular telephones, DTV and network computers, etc) for accessing online multimedia content. Developing appropriate multimedia content for each of the abovementioned devices that may also function in different networks is very expensive and somehow unmanageable. Content repurposing deals with this problem by taking multimedia content designed for a particular device or platform and automatically repurposing it to fit other platforms or devices. The basic idea is to maintain a single copy of the content in its original form and to separate the actual content from its presentation format. The approach proposed in this paper uses Web services technology to repurpose multimedia content to fit any desired device automatically. In our framework the multimedia content is stored in distributed databases and is described using metadata standards. Once the system receives a specific request, it compiles the response according to the user profile and end device. If a specific repurposing service is not available in the local system infrastructure (e.g., to transcode a tiff image into a png image), a Web services request is made to a specific UDDI server requesting an appropriate repurposing service. If a service is found we make use of its capabilities, otherwise we try to find a bridge to solve the request (e.g., transcoding tiff into jpg and then jpg into png). In this paper we describe the architecture of the system and its main functionality.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".