Bibliographic record
Abstract
Learn how to install, configure, and maintain the latest release of Microsoft’s popular SharePoint Server, SharePoint 2016. This latest version brings with it many changes for IT professionals. With this book you will learn how to create an efficient and stable SharePoint environment for your organization. Deploying SharePoint 2016 will help you: Learn the steps to install SharePoint Server 2016, using both the user interface provided by Microsoft, and PowerShell Understand your authentication options and associated security considerations Deploy add-ins, either from the store, or from your own custom app catalog Configure Search Service Application using either the provided user interface (UI) or PowerShell Configure business intelligence service applications such as SSRS, PowerPivot, and Performance Point Learn the steps to migrate to SharePoint Server 2016 from either SharePoint Server 2010 or 2013 Understand the approaches to high availability, disaster recovery, patching, and ways to monitor and maintain your SharePoint 2016 deployment once it’s up and running Who This Book Is For: Anyone tasked with installing, configuring, and maintaining SharePoint Server 2016 in their organization. This book assumes some working knowledge of previous release of SharePoint Server, such as SharePoint 2010 or SharePoint 2013.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.274 | 0.223 |
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".