A low energy measurement of the B solar neutrino spectrum at the Sudbury Neutrino Observatory
Bibliographic record
Abstract
Physics is enormous fun in large part due to the interesting people you get to work with.First and foremost, I would like to thank my advisor, Josh Klein, for making this thesis possible.Josh is the consummate experimental optimist, believing nearly any problem can be overcome with the right combination of clever ideas and hard work.He will happily supply a stumped graduate student with an endless list of crazy and creative suggestions, and entertain new ideas flowing the other direction as well.Much to the annoyance of said graduate student, however, he also refuses to allow people to give up when the right solution turns out to be more difficult than expected.Through it all, he treats his graduate students as real people and colleagues, not units of labor, a trait which is (sadly) less common among advisors than one might hope.Without a doubt, I cannot imagine a better advisor, or a better graduate experience. Down in the concrete bunker that is ENS 16N, Aubra Anthony, ChrisTunnell, Melissa Jerkins, and Julia Majors made the lab a pleasant and fun place to be.They always offered a sympathetic ear when things were not going well, and had great stories to liven up a dull afternoon.Chris taught me many things, including the proper air hose safety and alternate fillings for piñatas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".