Bibliographic record
Abstract
Problems in the field of data visualization are "how to define the layout algorithm to discover the hidden relationships in the financial data" and "how to design and develop a reliable system to implement the layout algorithm". Although there were many solutions to deal with the problems, the methods mainly focused on visualizing network topologies or social networks. This thesis develops a novel tree layout algorithm based on a power-law degree distribution and a robust flash web application system to investigate relationships in financial time-series data. Compared to previous tree layout algorithms, this novel layout algorithm is flexible, scalable, and easy to implement. Additionally, this thesis presents the design structure and important features in the novel graph visualization system. This system provides a number of unique characteristics. For example, if a new focus node is selected, the feature of animating zooming with degree separations will track an animating transition from one to another layout for users. At last, based on many experiments, the thesis demonstrates the profound applications and practices of this system. When utilizing the visualization system to display the data in world crude oil price, we discover that the price fluctuations of crude oil within Organization of the Petroleum Exporting Countries (OPEC) or non-OPEC group are correlated, but price fluctuations of crude oil between difference groups are uncorrelated.
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".