Nashville, Tennessee Approved by:
Bibliographic record
Abstract
First and foremost, I would like to thank my advisor and mentor Dr. Michael I. Miga. This work and my Ph.D. career would not have been possible but for his continued support. I would also like to thank him for being patient with my mistakes over the last few years. He continues to be a source of encouragement. I would also like to thank my other committe members: Dr. Bob Galloway, Dr. Reid Thompson, Dr. Benoit Dawant and Dr. Robert Roselli. I would like to thank Dr. Bob, Dr. Dawant and Dr. Thompson for always keeping their doors open for my questions. I would like to thank Dr. Roselli for agreeing to be a part of my committee at the last minute. I really appreciate all the time, efffort and inputs they have provided me over the course of my research. Acknowledgements are also due to the operating room staff and nurses at the Vanderbilt University Medical Center for assisting me in data collection. This work would not have been possible without the help of various members of the SNARL and BML labs (past and present). Many of them have been the sounding board for my ideas over the past few years. I would like to thank them for their help and friendship.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.705 | 0.426 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".