Bibliographic record
Abstract
Academically, finishing my PhD back in 2010 in Oslo, Norway. Sharing the moment with my family, friends and colleagues. As a sports physio, reaching the playoff finals of the Swiss Pro ice hockey championship with ‘my’ team (despite losing the Cup in game 7). My cousin Lucio (almost a brother for me), who played professional football (FC Servette) and studied at the University of Geneva with me. Wake up at 05:30–06:00, go to the clinic (office) and drink coffee while checking emails. At 7:30 I’ll have a small breakfast with some clinic colleagues, then start working on projects for the day. Working at the 2010 FIFA World Cup in South Africa (the first World Cup in the African continent). I don’t think it’s up to me to say this, but I hope to have contributed a bit to the promotion of injury prevention and sports physiotherapy internationally. Staying humble (and working hard). Mobilising the upper limbs of …
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.035 |
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".