Bibliographic record
Abstract
Welcome to the BPNA’s first virtual annual conference. It has been an unexpectedly difficult year for us all and it is a pity that we will not be able to meet up in person in Oxford as was originally planned for this, our 47th annual conference. However, in every cloud there is a silver lining, and the coronavirus pandemic has been a significant catalyst in the BPNA expanding its presence in the digital world and developing its abilities to deliver education and science in an on-line environment. There is no doubt that we have had a greater national and international reach as a result and even when we return to face-to-face meetings we will be incorporating the techniques and skills we have learned to deliver a virtual as well as in-person experience. I am therefore delighted that we are able to run our conference virtually this year and much credit must be given to the Oxford team and the BPNA Scientific for putting together a superb programme of new scientific research and state-of the-art presentations from key opinion leaders in the field of paediatrics and paediatric neurology. It gives me personal pleasure to welcome our international guests Professors Maya Steinlin from Berne, our Ronnie MacKeith Guest Lecturer, and Gabrielle Deveber from Toronto – both of whom are old friends and international experts in the field of paediatric cerebrovascular disease. Professor Russell Dale from Sydney is also well known to the BPNA having trained in the UK before his move to Australia. Russell is a superb speaker and a worldwide authority on neuroinflammation. It would be impossible not to devote some of our conference to the coronavirus and how it has impacted child neurology. We are particularly fortunate to have updates on Covid-19 from Professors Andrew Pollard, one of the leaders of the Oxford Vaccine Group who are developing a vaccine against the coronavirus, and Tom Solomon, a neurologist who is also Director of the University of Liverpool’s Institute of Infection and Global Health who will update us on the neurological aspects of the disease. Both Andrew and Tom will be familiar names to you all as they have had understandably high profiles in the media in the last 12 months. We may not be together in person, but the annual conference programme is stronger than ever: a cornucopia of scientific excellence and opinion. You are all warmly welcomed to our virtual experience, we hope you find it both enjoyable and informative. Until we meet again in person, take care and stay safe. With best wishes Finbar O’Callaghan BPNA President
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".