Bibliographic record
Abstract
my graduate studies. With his insightful guidance, positive attitude and his disposition to help me, no matter how busy he was, he has given me a perfect example of the consummate professional and wonderful human being. Special thanks go to Dr. Laurie A. Garrow, who since the first day of school welcomed me with open arms and taught me how to overcome the first few months in a new environment for me. Extended thanks to Dr. Jiawen Yang, who convinced me through example of pursuing a degree in City Planning. I would also like to thank the people who to a greater or lesser degree allowed me to work on such an engaging topic. My dearest friend David Uniman, as well as Carlos Gutiérrez and Darío Hidalgo from the EMBARQ network, who not only made data available to me but shared their wealth of knowledge and passion for transportation. Germán Lleras, my advisor at Los Andes and always a source of support. Also: Juanita Concha, From Transmilenio; the people from OC Transpo in Ottawa and Translink in Brisbane, and Transantiago, in Santiago; PAAC in Pittsburgh; and MBTA in Boston. Thank you for making it possible to work on this and my other research projects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads 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".