Bibliographic record
Abstract
After more than six years of funding exclusive projects in forestry and agriculture, Genome Canada has now announced a $67.5 million funding competition for large-scale genomics projects in human health, with focus on personalised medicine.i The human genomics community of this country understandably rejoiced at this long overdue announcement that gives them, for the first time in years, the means to compete internationally (http://www.genomecanada.ca/en/portfolio/research/2012-competition.aspx). Canada has been fairly generous in funding human-health related research but mostly through the Canadian Institutes of Health Research (CIHR) or the Canada Foundation for Innovation, neither of which has within its mandate to fund the type of specific multimillion-dollar project that is usually thought of as genomics. The typical CIHR grant, for example, rarely exceeds $1 million (200 000 over 5 years) in direct costs. The elation over the announcement of this funding opportunity in late 2011 soon gave way to sober reflection when prospective applicants started looking at the fine print. The terms of reference made it clear that this is a call for proposals with a very strong utilitarian angle, ‘capable of concrete deliverables by the end of the funding period that will have clinical utility and/or practical applicability’ and ‘social and/or economic benefits … realised within a short time-frame after the end of the project’, to quote from the official announcement. The funding period is four years (only coincidentally, I am sure, the time to the next federal election). How short the additional ‘short time-frame’ might be, is left to interpretation but the context leaves little doubt that it cannot be more than a couple of years. How else can applicants ‘… demonstrate end-user engagement in the development and execution of the research plan’? Examples of end-users whose interest must be attracted sufficiently for them to participate in the ‘execution of the research’ and be …
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 | 0.009 | 0.027 |
| 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 | 0.002 |
| 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".