Bibliographic record
Abstract
Features 14 Theory Meets Practice at Huizenga School 16 Nursing Department Makes Huge Strides in Short Time 20 Society’s Problems Find Solutions Here 24 Medical Missions: Serving the Underserved 26 A True University Experience, Far from Main Campus 32 NSU Broadcasts the First Voice in Law School Radio Departments 2 Letter from the President 3 Academic Notes New Beginnings for Our Treasured Reefs International Coral Reef Symposium $500,000 Donated to Scholarships for High-Achieving Transfer Students Graduate Program Catches the Information Technology Wave Master’s Degree Program Responds to Multiculturalism in the Classroom University Center Gets the Ball Rolling for Intramural Sports 6 Around Campus Genealogical Society Gifts Private Collection to NSU Sharks Baseball Team Participates in Miracle League World Series History in the Making: Time Capsule Buried in University Center Black Box Theater Plays a Leading Role on Campus University School Unveils Site Plans on the Horizon Alvin Sherman Library Takes to the Stage MSI Toddlers, Preschoolers Learn How to Go Green 10 Spotlight College of Osteopathic Medicine Doctor Makes a Difference Dean Harbaugh Resigns, Professor Harbaugh Enters the Classroom 28 Verbatim Fighting Smart: An Educator’s Mission to Prevent Bullying 30 Alumni Journal Kenneth and Josh Rader—The Cereal Bowl Gregory M. Vecchi, Ph.D.—Class of 2006 34 Scoreboard Sharks Athletics Receives Support and Achieves Success
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.651 | 0.382 |
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".