Study and implementation of beam lines and devices \nfor the acceleration, transport and manipulation \nof laser-accelerated particle beams.
Bibliographic record
Abstract
Undertaking the path of academic research as an alternative to a more conventional professional position as an engineer has not been straightforwardly planned, easy choice.After having graduated from my Master's degree, in July 2013, I did two promising job interviews and nothing seemed to lead me to extending my university studies further.What I had not taken into account were the persuasive skills of Patrizio Antici who had been my thesis supervisor for the previous six months.He had made me familiar with the topic of proton acceleration via laserplasma interaction and collaborating with him had given very good results.During a lunch break at the department of Applied Science for Engineering of the university of Rome "La Sapienza" we had the conversation that would have determined the following three and a half years of my life, at least from the scientific point of view.He told me about the interesting prospects of continuing the work that we had started together and suggested me to apply for a doctoral student position at "La Sapienza".My journey through diverging electron beams and unreliable proton sources, too high emittances and unwieldy beam lines, unclear manuscripts and fussy referees, had just begun.The collaboration with Patrizio became even stronger about one year later, as he accepted to become my thesis supervisor at the Institut National de la Recherche Scientifique, when I signed an agreement for an Italian-Canadian joint doctorate.Doing my Ph.D. under Patrizio's direction has been an extremely rewarding experience, from the scientific and personal point of view, that I would repeat at any time.This is the reason why the first acknowledgment spontaneously goes to Patrizio, for having guided me throughout these years, for having given me some hard and challenging times but also often enjoyable and amusing moments, for the many advises and the mentoring, and for the continuous support he has given and still is giving me today.I truly thank him for everything.My doctoral studies would not have been possible without the endorsement and the supervision of Prof. Luigi Palumbo, the thesis director at my home university in Rome.He has welcomed me in his research group and has always supported my work and my initiatives, driving me to the pursuit of valuable scientific results and cultural growth.During this experience I have had the possibility of cooperating with the particle accelerator group of "La Sapienza", learning from some of the best scientists operating in this field.It is impossible to list them all, but among them, I would like to thank especially Mauro Migliorati, Andrea Mostacci, Livia Lancia and Luca Ficcadenti, who I have had the pleasure to work with in a continuous way.I would like to thank the staff members of the Canadian institute INRS, the partner university for the joint supervision of my Ph.D., I have had the honor to collaborate with.Among them, a special
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".