Bibliographic record
Abstract
All along my PhD at Caltech I have been the lucky receiver of many people’s support. I am now facing the difficult task of verbally exposing my deepest thanks. If by reading this you have the impression I am thanking half of the planet, you are probably right! But, hey, as long as the acknowledgements are not longer than the thesis, I am sticking to the ‘etiquette’. It seems like this is the first time that I have the opportunity to officially thank the individuals who have in one way or another helped me get where I am today. So I will start at the beginning, thanking Alfredo Torruella and Antonio Algaze, who during my early undergrad years at the University of Puerto Rico were key in captivating my attention in Physics; and Carmen Pantoja, thanks to whom I had the great chance of working at the fantastic Arecibo observatory (can anyone guess a slight bias?) and who has followed all of my adventures in the world of astronomy. I want to give special thanks to: Blanca Silvestrini, my earliest and constant mentor; Vicky Kaspi, with whom I had the chance of overlapping at McGill just before I graduated; Karl-Heinz Mack, with whom I worked at ASTRON in the Netherlands during the summer in which I discovered that astronomy had entered my
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.397 | 0.369 |
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".