An interview with David Normando
Bibliographic record
Abstract
There are several yardsticks to gauge a man's worth.One of them is, undoubtedly, the respect he earns from his peers.As I organized the questions to be sent to Professor David Normando, made by some of the greatest thinkers in the field of contemporary Orthodontics, I saw the reflex of such respect on the profound inquiring, to which so few would be capable of responding with so much propriety.He has also proven that the stars that shine brightest in the celestial sphere are not restrict to the borders of certain countries or regions.They may become references to us all, wherever they are or come from.However, I must confess that I feel especially proud to know that this icon of Orthodontics had to follow a hard and tortuous path, like the bayou.In the public schools where he studied, in remote cities of the Brazilian Amazon region, his education in Orthodontics required bus travels between Belém and Bauru, which together added up to several journeys around the Earth.On that note, I leave you with a few brief snapshots of his fascinating life history.David has a unifying personality that brings friends closer and organizes teamwork with clockwork precision.This quality has greatly contributed to the growth of scientific production in Dentistry in the Northern Region of Brazil, which culminated in the establishment of the first Doctorate Program in Dentistry in the region.He also has the privilege to have Thiene by his side, his wife and confidant, with whom he had two children, Gabriel and Matheus.A beautiful family that professes the ethics of hard and honest work and happiness.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".