Bibliographic record
Abstract
April 2010 Newsletter for Financial Services Table of Contents: In this issue: * From the Desk of Dr. Roy ASaP (Auxiliary Services and Programs): * Tech Manager Wins Award * How Social are We? * Bookstore Debuts iPad * Bookstore Partnership * Copy/Print Contacts * The Commons for Summer * Summer Asphalt Planning * Crosswalk at Central Deck Budget & Planning: * Budget Process Continues Business Services: * Conference Agendas & Provided Meals * Planning a Trip? * Bill + Payment by Mid-April * New Waiver Forms * Fund Groups * What's Not Allowed? * New Works Passwords * Procurement Survey * What's Up in Procurement? * Congratulations! * FY 2010 YE Deadlines Card Services: * Marketplace Upswing * Help Us Help You * Friend Card Services! PMO (Project Management Office): * Identity Theft Prevention Also this month: * Meet this Florida native, adventurer and artist, Assistant to the Vice President, Toni Waters * Get to know this multi-lingual Canadian, Cashier Manager Patsy Lai Guest Columns: * Yesterday, Today, Tomorrow by Dr. Ernesto Pierre Silva * Coles College Launches Partnership with b-school in India * Confucius Institute Progress Report by Ken Jin
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.604 | 0.552 |
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".