Data visualization and crowdsourcing approaches for complex data analysis
Bibliographic record
Abstract
Data visualization and crowdsourcing approaches for complex data analysisby Alexander BUTYAEV State-of-the-art machine learning algorithms (e.g., convolution neural networks, long short term memory recurrent networks) have allowed human-driven analysis to become completely automated.However, there is an increasing need for human supervision and intuition when automating these analyses.The objective of this thesis is to analyze crowdsourcing and human computation in Human-Computer Interaction (HCI) systems, which engage human participants to efficiently solve problems that are hard for computers but intuitive for humans.This thesis addresses questions related to problems of data decomposition, visualization, and interpretation from the perspective of HCI.Solution assembly strategies, as well as user motivation, will also be explored.First, to inform the design of HCI systems, data visualization techniques are explored.The goal of this project is to design HCI systems that are convenient for humans and allow data to be easily interpreted.Genomics was a natural choice for this study as the field explore complex data sets (e.g., Hi-C and Experimental ChIP-Sequencing data) that require advance visualization to allow a user to interpret the data.Here, we describe 3DGB, an interactive web-based 3D genome browser that is
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".