First Look at Microsoft SQL Server Yukon Beta for Developers
Bibliographic record
Abstract
Be the first to master SQL Server 2005's breakthrough database development capabilitiesFew technologies have been as eagerly anticipated as Microsoft SQL Server 2005 (Yukon). Now, three SQL Server insiders deliver the definitive hands-on preview--accurate, comprehensive, and packed with examples.A First Look at SQL Server 2005 for Developers starts where Microsoft's white papers and Web articles leave off, showing working developers how to take full advantage of Yukon's key innovations. It draws on exceptional cooperation from Microsoft's Yukon developers and the authors' hands-on access to Yukon since its earliest alpha releases.You'll find practical explanations of Yukon's new data model, built-in .NET hosting, improved programmability, SQL-99 compliance, and much more. Virtually every key concept is illuminated via sample code tested with Microsoft's public beta.Key coverage includes: Yukon as .NET runtime host: enhancing security, reliability, and performance Writing procedures, functions, and triggers in .NET languages Leveraging powerful new enhancements to T-SQL The XML data type and XML query languages SQL Server 2005 as a Web Services platform Client-side coding: ADO/ADO.NET enhancements, SQLXML, mapping, ObjectSpaces, and more Using SQL Server 2005's built-in application server capabilitiesAlready committed to SQL Server 2005? Simply evaluating it? Looking to set yourself apart from other SQL Server developers? Whatever your goal, start right here--today. 0321180593B04152004
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.332 | 0.450 |
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".