Iterative K-Line Meshing Non-Linear Least Squares Interpolation of Affectively Decorated Media Repositories
Bibliographic record
Abstract
We present an algorithm that organizes a song repository upon recording a user’s memory experiences from previous music listening activities. Our method forms an affectively annotated network of songs. The network’s connections correspond to a person’s recorded memory experiences related to song preferences when the person is at different states of affective bias. Upon formation of this network, an intelligent affect-sensitive network navigation algorithm synthesizes playlists that conform to desired affective states. The method for the network formation is highly individualized, in the sense that it takes in account an individual’s music preferences which are typically subjective and may differ from user to user. Also, the method is content independent, in the sense that it does not rely or favor any particular music genre. In fact, the method is applicable to any type of media, not only songs. We implement our method and present evaluation results from the introspection of our algorithms’ execution and from feedback recorded during the evaluation by human test subjects. The evaluation results clearly indicate that the proposed method significantly outperforms the most typical paradigm of random song selection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".