Bibliographic record
Abstract
We are delighted to welcome our international and national presenters, delegates from cancer research groups in Australia and across the world, and our generous sponsors to the fourth Sydney Cancer Conference (SCC2014), hosted by the University of Sydney Cancer Research Network (CRN). The CRN currently has more than 800 members ranging from PhD students to leaders of major research programs across multiple disciplines. The theme of our biennial conference is ‘Bridging Research and Practice’, which recognises the rapid advances being made in basic research and the need for their translation from the bench to the beside and also translation of the evidence generated into clinical practice. We wish to welcome our eminent keynote speakers: Professor Sir David Lane, Chief Scientist, A*STAR and Director p53 Laboratory, Singapore; Professor Eva Grunfeld, Vice-Chair Research, University of Toronto, Canada; Professor Fiona Watt, Director, Centre for Stem Cells and Regenerative Medicine, King's College London, UK; and Professor Dennis Lo, Director, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong. Australian scientists make a disproportionately high contribution to international health and medical research and this is reflected in the number of national speakers at SCC2014. The conference will open with a public lecture delivered by Professor Ian Frazer (Chief Executive Officer and Director of Research, Translational Research Institute, Brisbane, Australia) and each of the conference's themes is carried forward by distinguished national speakers. Invited posters will be on view during the conference with the main viewing session at our cocktail function on Thursday night. Our workshops on ‘Career hints for postgraduates’ and ‘Talking to commercial partners – when and how?’ further complement our program. Early career researchers and postgraduate students will be in the running for oral and poster awards in recognition of quality research. A panel discussion on Friday afternoon will address the topic ‘Smashing silos, building bridges – is multidisciplinary research delivering on its promise?’ We aim through this forum, with active delegate participation, to debate key translational research questions and thereby help direct our efforts towards greater impact. The panel will be chaired by ABC broadcaster and University of Sydney's Ambassador for Mathematics and Science, Adam Spencer. His humour and scientific insights will make for a fascinating panel discussion. It has been our privilege to convene this conference. Our sincere thanks, to the conference organising committee and in particular Merilyn Heuschkel, Executive Officer, Cancer Research Network, for their dedication and hard work in creating an excellent scientific program. SCC2014 was been supported by a number of sponsors to whom we are very grateful. Please take the time to visit their exhibition stands and read the information in your conference satchels from our supporters. We welcome you to Sydney and hope that this year's conference will challenge and inspire you, and result in new knowledge, collaborations, and friendships.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.084 | 0.072 |
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".