Bibliographic record
Abstract
Eclipse DistilledDavid CarlsonForeword by Grady BoochSeries EditorsErich Gamma Lee Nackman John WiegandA Concise Introduction to Eclipse for the Productive ProgrammerOrganized for rapid access, focused on productivity, Eclipse Distilled brings together all the answers you need to make the most of today's most powerful Java development environment. David Carlson introduces proven best practices for working with Eclipse, and shows exactly how to integrate Eclipse into any Agile development process.Part I shows how to customize workspaces, projects, perspectives, and views for optimal efficiency-and how to leverage Eclipse's rapid development, navigation, and debugging features to maximize both productivity and code quality. Part II focuses entirely on Agile development, demonstrating how Eclipse can simplify team ownership, refactoring, continuous testing, continuousintegration, and other Agile practices. Coverage includes Managing Eclipse projects from start to finish: handling both content and complexity Using perspectives, views, and editors to work more efficiently Setting preferences to fit your own unique needs-or your team's Leveraging Eclipse's powerful local and remote debugging tools Understanding how Eclipse fits into contemporary iterative development processes Performing continuous testing with JUnit in the Eclipse environment Using Eclipse's wizard-assisted refactoring tools Implementing continuous integration with Ant-based automated project builders Employing best practices for code sharing with CVS and other repositoriesBy focusing on need-to-know information and providing best practices and methodologies, this book is designed to get you working with Eclipse quickly. Whether you're building enterprise systems, Eclipse plug-ins, or anything else, this concise book will help you write better code-and do it faster.About the AuthorDavid Carlson is a developer, researcher, author, instructor, and consultant who thrives on innovative technology. He started using Java in 1995 and Eclipse in 2001. David has a Ph.D. in Information Systems from the University of Arizona and is a frequent speaker at conferences and a contributor to technical journals. He is creator of the hyperModel plug-in for Eclipse, and author of Modeling XML Applications with UML (Addison-Wesley, 2001).Cover photo: © archivberlin Fotoagentur GmbH / AlamyAddison-Wesley www.awprofessional.com/series/eclipseISBN 0-321-28815-7$34.99 US $48.99 CANADA© Copyright Pearson Education. All rights reserved.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.200 | 0.240 |
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".