Chants and Orcas
Bibliographic record
Abstract
The recent explosion of web-based collaborative applications in business and social media sites demonstrated the power of collaborative internet scale software. This includes the ability to access huge datasets, the ability to quickly update software, and the ability to let people around the world collaborate seamlessly. Multimedia learning techniques have the potential to make unstructured multimedia data accessible, reusable, searchable, and manageable. We present two different web-based collaborative projects: Cantillion, and the Orchive. Cantillion enables ethnomusicology scholars to listen and view data relating to chants from a variety of traditions, letting them view and interact with various pitch contour representations of the chant. The Orchive is a project to digitize over 20,000 hours of Orcinus orca (killer whale) vocalizations, recorded over a period of approximately 35 years, and provide tools to assist their study. The developed tools utilize ideas and techniques that are similar to the ones used in general multimedia domains such as sports video or news. However, their niche nature has presented us with special challenges as well as opportunities. Unlike more traditional domains where there are clearly defined objectives one of the biggest challenges has been the desire to support researchers to formulate questions and problems related to the data even when there is no clearly defined objective.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".