Research Magazine Spring 2008 - Focus: Canada Foundation for Innovation
Bibliographic record
Abstract
In this issue: A decade of CFI funding; Supercomputer network battles disease spread; Centre supports broad spectrum of research; Getting the scoop on neck pain; Osteoarthritis: Bad to the bone; Learning to stand on your own two feet; The feline connection to AIDS; Enhancing breast cancer treatment; The search for new antibiotics; Epilepsy research advancing with new technology; Looking for the neurological cause of nausea; A new way to manage weight; Where electrical and chemical energy meet; The effect of vision on walking; Calling all tourists; A literary trip across Canadian cultures; From electronics to spintronics; Tomatosphere brings outer-space tomatoes inside the classroom; Re-creating the red planet; Measuring tiny particles; The challenges of climate change; Protecting one of the world's great sport fisheries; Controlling the lamprey; Preserving freshwater resources; Recycled waste water to help protect water resources; Interrupting heart failure; A cue from bacteria; A new method for cataloguing Earth's species; Developing genetic resistance to Marek's disease; Stress during pregnancy can cause lasting effects; Winning the fight against bacterial blight; Institute for food safety keeps bacteria at bay; The dangers of multi-tasking while driving; Technology, the artist and the internet
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.324 | 0.172 |
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".