How Tony Melcher advanced our understanding of periodontal biology and regeneration
Bibliographic record
Abstract
Tony Melcher, a highly influential and forward-thinking scientist and teacher, focussed on the origins, behaviour and regulation of cells in periodontal tissues. His recent death in April 2020, has motivated us to highlight his multi-level contributions to research in biology and the dental sciences. Tony was particularly adept at recognizing the inherent instructive power of the periodontium, most notably as a model system for studying the inter-relationships between the structure, development and functions of connective tissues. Further, his mentoring of dozens of students who subsequently went on to develop their own careers in research, and his leadership in promoting collaborations in dental sciences world-wide, engendered important advances in the importance and utility of research relating to oral tissues. Here, we reflect upon his development of a large, multi-disciplinary research enterprise, the MRC Group in Periodontal Physiology at the University of Toronto and brief commentaries of those who worked with him there. We examine his early career development and then go on to consider some of his most highly cited publications and their impact on subsequent research trends.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".