Bibliographic record
Abstract
Matlab is an extremely useful tool during the development and testing of a wide variety of applications. With built in ready-to-use functions that have been optimized for fast execution, and easy access to toolboxes and user generated contributions, it is possible to quickly implement and test various approaches before committing to a single one during the research and development process. In addition, if used properly, Matlab's graphical user interface (GUI) and display functions can help visualize your data without spending hours coding in more complex languages and still retain the complexity provided by these alternatives. This workshop will address using Matlab, Matlab Toolboxes and user contributed functions specifically with respect to image processing, analysis and display. We will review which related functions are included in Matlab, how to use them properly, and what the limitations of these functions are. We will also go over some strategies for implementing your own functions, identifying bottlenecks, and some suggestions on how to use the GUI and related functions to present your data. Examples of Matlab code used in the development of a deep vein thrombosis screening system will be presented. It is expected that participants will be somewhat familiar with coding in Matlab, and have an interest in image processing and analysis.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.081 |
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".