Bibliographic record
Abstract
When Ron Droste, my colleague Editor-in-Chief, announced last year he felt it was time for him to step down and give a chance to somebody else to team up with me as Editor-in-Chief, a little panic attack followed. How do you replace a devoted, experienced and meticulous person as Ron? Well, we found a person that comes with an extraordinary skill set: Dr Arash Zamyadi, recently appointed assistant professor at Polytechnique Montreal. He is young and eager and has shown his devotion to the International Water Association in many ways, as recognized with the IWA Fellowship he obtained in 2016. For instance, in 2010 he initiated the North American chapter of IWA's Young Water Professionals. People that know him, can certainly vouch for his optimism, his incredible energy and high emotional intelligence quotient. However, in order to take up the challenging task of being Editor-in-Chief of a peer-reviewed journal that seeks to increase its impact, an excellent scientific reputation is key and the wide scope …
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.037 | 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".