Bibliographic record
Abstract
Noah Bergmann won this years Acoustic award at science fair due to his work on the "Musician's Toolbox".Noah Bergmann has been programming in various programming languages since the age of 8, releasing his first iPhone app at the age of 15.On top of being a general computer enthusiast Noah is also passionate about music.He plays the bass, ukulele, and guitar in his spare time.Living in a small town Noah enjoys hikes out into the forest on the numerous trails surrounding his hometown.Noah began science fair at the age of 10, with his first two projects focusing on biology.However in his third year he moved to computer science, and has been met with more success, going to national science fair in 2012, 2013 and 2014 becoming the top prize winner in BC in his most recent year.Once graduated Noah plans to study computer science with a specialization in software engineering.Noah Bergmann a remport cette anne le prix acoustique l'exposicences pancanadienne en raison de ses travaux sur la Bote outils du Musicien".Noah Bergmann a fait de la programmation dans divers langages depuis l'ge de 8, ralisant sa premire application iPhone l'ge de 15 ans.En plus d'tre un passionn d'ordinateur, Noah est aussi passionn de musique.Il joue de la basse, du ukull et de la guitare dans son temps libre.Noah vit dans une petite ville o il aime faire des randonnes dans la fort sur les nombreux sentiers l'entour.No participa pour la premire fois expo-sciences l'ge de 10, avec ses deux premiers projets en biologie.En troisime anne, il est pass l'informatique, et a t accueilli avec plus de succs lors des expo-sciences de 2012, 2013 et 2014 en devenant le gagnant du premier prix en Colombie-Britannique ces dernires annes.Une fois diplm, Noah prvoit d'tudier l'informatique avec une spcialisation en gnie logiciel.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".