Bibliographic record
Abstract
As you read this edition of the International Wound Journal (IWJ), the first World Union of Wound Healing Societies (WUWHS) meeting of this new decade will just have happened or be taking place in Abu Dhabi, United Arab Emirates.This is the 6th WUWHS meeting, which is essentially the Olympics of Wound Healing, taking place every 4 years.It seems like a lifetime since the Toronto meeting in which I played a significant part.Ironically, at that time (2008), the world was dealing with SARS, and here we are again, dealing with the coronavirus.Understanding how it impacted our conference, I wish the organisers for a successful conference.Unfortunately, because of some minor health issues, our Editor will be unable to attend, but look out for our Editor-in-Chief.The objective of WUWHS 2020 is to promote greater collaboration and cooperation of all the Scientific Wound Care Societies around the world who have passion for caring for wounds as their main mission.WUWHS 2020 will offer an extensive scientific programme, including numerous symposia, training sessions, workshops, and focus sessions, with leading international experts in the field of wound management.WUWHS 2020 will be an important moment of cultural unity, an essential scientific round- table and
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.127 | 0.145 |
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".